This dataset contains the London subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/london-attraction.csv). The original dataset comprises 20,727 entries that encompass a variety of attractions across London, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.

However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 2,341 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of London's attractions.

• London.csv It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.

• London_annotated.csv It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories. It has the following columns:

  • POI name
  • POI's address
  • One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.

London’s Tourism Direct GVA (TDGVA) in 2019 is estimated by taking London’s share of the latest ONS regional TDGVA publication (2013) and multiplying it with the latest TDGVA estimate (2017) for the UK. The resulting figure is then multiplied with a ratio of London spend data from VisitBritain’s International Passenger Surveys from 2019 and 2017. This approach assumes 1) that London’s share of UK TDGVA has remained constant from 2013 to 2017 and 2) a constant relation between spend and GVA from 2017 to 2019. The annual VisitBritain forecast for the volume and value of inbound tourism to the UK is issued in December each year. They have however updated this to reflect the impact of COVID-19 on inbound tourism to the UK, as well as an estimate of impact on domestic tourism within England. These were used to estimate London’s tourism spend for 2020. More details on the inbound tourism forecast for 2020 to the UK and domestic tourism with in England for 2020 can be found at Visit Britain.

In 2023, London was the leading European city tourism destination based on the number of bed nights. That year, bed nights in the United Kingdom's capital exceeded 78 million, denoting a sharp annual increase but not fully recovering yet from the impact of COVID-19. Meanwhile, Paris and Istanbul followed in the ranking in 2023, with roughly 52 million and nearly 30 million bed nights. What are the most visited countries in Europe? While the French capital came in second among leading European cities based on bed nights, France topped the ranking of the European countries with the highest number of inbound tourist arrivals in 2023, ahead of Spain, Italy, and Turkey. Meanwhile, when looking at European countries with the highest tourism receipts that year, Spain recorded the highest figure, with over 90 billion U.S. dollars, followed by the United Kingdom. How many international tourists visit Europe every year? In 2023, the number of international tourist arrivals in Europe grew significantly over the previous year, totaling over 700 million. This figure, however, remained below pre-pandemic levels. Overall, either before and after the impact of COVID-19, Europe was the region with the highest number of international tourist arrivals worldwide.

London's Airbnb

Airbnb listings in London

About this dataset

This dataset provides information on Airbnbs in London. Each row represents one listing, and there are a variety of columns with information on the listing, such as the name, host, price, etc.

This dataset could be used to study patterns in Airbnb pricing, to understand how Airbnbs are being used in London, or to compare different neighborhoods in London

How to use the dataset

If you're looking for information on Airbnbs in London, this dataset is a great place to start. It provides information on the listings and reviews for Airbnb in the city of London.

Airbnb is a popular vacation rental platform that allows travelers to find and book accommodations around the world. With over 3 million listings in more than 65,000 cities, Airbnb has something for everyone.

London is one of the most popular tourist destinations in the world, and Airbnb offers a unique way to experience the city. With so many different neighborhoods to choose from, there's an Airbnb listing for everyone.

This dataset includes information on the listing price, minimum nights required, number of reviews, and more. With this data, you can begin to understand how people are using Airbnb in London and what factors affect pricing. So whether you're looking for a place to stay during your next trip or just curious about how Airbnb is being used in different cities, this dataset is for you!

Research Ideas

• If there's a relationship between the price per listing and how long it is available on Airbnb, this could be used to recommend lower prices for listings that are unlikely to stay booked for very long periods of time.
• There might be a relationship between the number of reviews per month and the calculated host listings count. If there is, this information could be used to help improve customer satisfaction by either recommending that hosts with lots of listings receive more reviews or that they stagger their listing availabilities so that they can provide better service.
• The neighbourhood data could be used to cluster listings into areas with similar characteristics, which would then allow customers to easily find similar listings in different areas of the city based on their preferences

Acknowledgements

This dataset is brought to you by Kelly Garrett. If you use it in your research, please cite her Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: listings.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------| | name | The name of the listing. (String) | | host_name | The name of the host. (String) | | neighbourhood_group | The neighbourhood group the listing is in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | room_type | The type of room. (String) | | price | The price of the listing. (Integer) | | minimum_nights | The minimum number of nights required to stay at the listing. (Integer) | | number_of_reviews | The number of reviews for the listing. (Integer) | | last_review | The date of the last review. (Date) | | reviews_per_month | The number of reviews per month. (Float) | | calculated_host_listings_count | The number of listings the host has. (Integer) | | availability_365 | The number of days the listing is available in a year. (Integer) |

File: reviews.csv | Column name | Description | |:----------------|:--------------------------------------| | last_review | The date of the last review. (String) |

File: neighbourhoods.csv | Column...

🇬🇧 영국 English London’s Tourism Direct GVA (TDGVA) in 2019 is estimated by taking London’s share of the latest ONS regional TDGVA publication (2013) and multiplying it with the latest TDGVA estimate (2017) for the UK. The resulting figure is then multiplied with a ratio of London spend data from VisitBritain’s International Passenger Surveys from 2019 and 2017. This approach assumes 1) that London’s share of UK TDGVA has remained constant from 2013 to 2017 and 2) a constant relation between spend and GVA from 2017 to 2019. The annual VisitBritain forecast for the volume and value of inbound tourism to the UK is issued in December each year. They have however updated this to reflect the impact of COVID-19 on inbound tourism to the UK, as well as an estimate of impact on domestic tourism within England. These were used to estimate London’s tourism spend for 2020. More details on the inbound tourism forecast for 2020 to the UK and domestic tourism with in England for 2020 can be found at Visit Britain.

TfL statement: We've committed to making our open data freely available to third parties and to engaging developers to deliver new products, apps and services for our customers. Over 11,000 developers have registered for our open data, consisting of our unified API (Application Programming Interface) that powers over 600 travel apps in the UK with over 46% of Londoners using apps powered by our data. This enables millions of journeys in London each day, giving customers the right information at the right time through their channel of choice. Why are we committing to open data? Public data - As a public body, our data is publically owned Reach - Our goal is to ensure any person needing travel information about London can get it wherever and whenever they wish, in any way they wish Economic benefit - Open data facilitates the development of technology enterprises, small and medium businesses, generating employment and wealth for London and beyond Innovation - By having thousands of developers working on designing and building applications, services and tools with our data and APIs, we are effectively crowdsourcing innovation How is our open data presented? Data is presented in three main ways: Static data files - Data files which rarely change Feeds - Data files refreshed at regular intervals API (Application Programming Interface) - Enabling a query from an application to receive a bespoke response, depending on the parameters supplied. Find out more about our unified API. Data is presented as XML wherever possible.

Comprehensive YouTube channel statistics for Joolz Guides - London History Walks - Travel Films, featuring 317,000 subscribers and 42,884,542 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in GB. Track 286 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The spreadsheet shows numbers and percentages of people in work aged 16-74 who travel to work by bicycle for all wards in London, from 2001 and 2011 Census. Included percentage point change, and rankings. Top 10 Wards in 2011: Queensbridge, Hackney, +19.1% Clissold, Hackney, +18.9% Stoke Newington Central, Hackney, +18.8% Dalston, Hackney, +18.3% Hackney Downs, Hackney, +17.7% Hackney Central, Hackney, +16.9% Leabridge, Hackney, +15.9% Victoria, Hackney, +15.8% Chatham, Hackney, +14.8% Wick, Hackney, +14.6%

Daytime population - The estimated number of people in a borough in the daytime during an average day, broken down by component sub-groups.

The figures given are an average day during school term-time. No account has been made for seasonal variations, or for people who are usually in London (resident, at school or working), but are away visiting another place.

Sources include the Business Register and Employment Survey (BRES) (available under license), Annual Population Survey (APS), 2011 Census, Department for Education (DfE), International Passenger Survey (IPS), GB Tourism Survey (GBTS), Great Britain Day Visit Survey (GBDVS), GLA Population Projections, and GLA Economics estimates (GLAE).

The figures published in these sources have been used exactly as they appear - no further adjustments have been made to account for possible sampling errors or questionnaire design flaws.

Day trip visitors are defined as those on day trips away from home for three hours or more and not undertaking activities that would regularly constitute part of their work or would be a regular leisure activity.

International visitors – people from a country other than the UK visiting the location;
Domestic overnight tourists – people from other parts of the UK staying in the location for at least one night.
All visitor data is modelled and unrounded.

This edition was released on 7 October 2015 and replaces the previous estimates for 2013.

GLA resident population, 2011 Census resident population, and 2011 Census workday populations (by sex) included for comparison.

See a visualisation of this data using Tableau.

For more workday population data by age use the Custom Age-Range Tool for Census 2011 Workday population , or download data for a range of geographical levels from NOMIS.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by method used to travel to work (2001 specification) and by distance travelled to work. The estimates are as at Census Day, 21 March 2021.

_As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Due to methodological changes the ‘mainly work at or from home: any workplace type’ category has a population of zero. Please use the transport_to_workplace_12a classification instead. Read more about this quality notice._

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Method used to travel to workplace

A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.

"Work mainly from home" applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.

Distance travelled to work

The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.

“Work mainly at or from home” is made up of those that ticked either the "Mainly work at or from home" box for the address of workplace question, or the “Work mainly at or from home” box for the method of travel to work question.

Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.

Combine this variable with “Economic activity status” to identify those in employment at the time of the census.

This data is experimental, see the ‘Access Constraints or User Limitations’ section for more details. This dataset has been generalised to 10 metre resolution where it is still but the space needed for downloads will be improved.A set of UK wide estimated travel area geometries (isochrones), from Output Area (across England, Scotland, and Wales) and Small Area (across Northern Ireland) population-weighted centroids. The modes used in the isochrone calculations are limited to public transport and walking. Generated using Open Trip Planner routing software in combination with Open Street Maps and open public transport schedule data (UK and Ireland).The geometries provide an estimate of reachable areas by public transport and on foot between 7:15am and 9:15am for a range of maximum travel durations (15, 30, 45 and 60 minutes). For England, Scotland and Wales, these estimates were generated using public transport schedule data for Tuesday 15th November 2022. For Northern Ireland, the date used is Tuesday 6th December 2022.The data is made available as a set of ESRI shape files, in .zip format. This corresponds to a total of 18 files; one for Northern Ireland, one for Wales, twelve for England (one per English region, where London, South East and North West have been split into two files each) and four for Scotland (one per NUTS2 region, where the ‘North-East’ and ‘Highlands and Islands’ have been combined into one shape file, and South West Scotland has been split into two files).The shape files contain the following attributes. For further details, see the ‘Access Constraints or User Limitations’ section:AttributeDescriptionOA21CD or SA2011 or OA11CDEngland and Wales: The 2021 Output Area code.Northern Ireland: The 2011 Small Area code.Scotland: The 2011 Output Area code.centre_latThe population-weighted centroid latitude.centre_lonThe population-weighted centroid longitude.node_latThe latitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_lonThe longitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_distThe distance, in meters, between the population-weighted centroid and the nearest Open Street Map “highway” node.stop_latThe latitude of the nearest public transport stop to the population-weighted centroid.stop_lonThe longitude of the nearest public transport stop to the population-weighted centroid.stop_distThe distance, in metres, between the population-weighted centroid and the nearest public transport stop.centre_inBinary value (0 or 1), where 1 signifies the population-weighted centroid lies within the Output Area/Small Area boundary. 0 indicates the population-weighted centroid lies outside the boundary.node_inBinary value (0 or 1), where 1 signifies the nearest Open Street Map “highway” node lies within the Output Area/Small Area boundary. 0 indicates the nearest Open Street Map node lies outside the boundary.stop_inBinary value (0 or 1), where 1 signifies the nearest public transport stop lies within the Output Area/Small Area boundary. 0 indicates the nearest transport stop lies outside the boundary.iso_cutoffThe maximum travel time, in seconds, to construct the reachable area/isochrone. Values are either 900, 1800, 2700, or 3600 which correspond to 15, 30, 45, and 60 minute limits respectively.iso_dateThe date for which the isochrones were estimated, in YYYY-MM-DD format.iso_typeThe start point from which the estimated isochrone was calculated. Valid values are:from_centroid: calculated using population weighted centroid.from_node: calculated using the nearest Open Street Map “highway” node.from_stop: calculated using the nearest public transport stop.no_trip_found: no isochrone was calculated.geometryThe isochrone geometry.iso_hectarThe area of the isochrone, in hectares.Access constraints or user limitations.These data are experimental and will potentially have a wider degree of uncertainty. They remain subject to testing of quality, volatility, and ability to meet user needs. The methodologies used to generate them are still subject to modification and further evaluation.These experimental data have been published with specific caveats outlined in this section. The data are shared with the analytical community with the purpose of benefitting from the community's scrutiny and in improving the quality and demand of potential future releases. There may be potential modification following user feedback on both its quality and suitability.For England and Wales, where possible, the latest census 2021 Output Area population weighted centroids were used as the starting point from which isochrones were calculated.For Northern Ireland, 2011 Small Area population weighted centroids were used as the starting point from which isochrones were calculated. Small Areas and Output Areas contain a similar number of households within their boundaries. 2011 data was used because this was the most up-to-date data available at the time of generating this dataset. Population weighted centroids for Northern Ireland were calculated internally but may be subject to change - in the future we aim to update these data to be consistent with Census 2021 across the UK.For Scotland, 2011 Output Area population-weighted centroids were used as the starting point from which isochrones were calculated. 2011 data was used because this was the most up-to-date data available at the time of work.The data for England, Scotland and Wales are released with the projection EPSG:27700 (British National Grid).The data for Northern Ireland are released with the projection EPSG:29902 (Irish Grid).The modes used in the isochrone calculations are limited to public transport and walking. Other modes were not considered when generating this data.A maximum value of 1.5 kilometres walking distance was used when generating isochrones. This approximately represents typical walking distances during a commute (based on Department for Transport/Labour Force Survey data and Travel Survey for Northern Ireland technical reports).When generating Northern Ireland data, public transport schedule data for both Northern Ireland and Republic of Ireland were used.Isochrone geometries and calculated areas are subject to public transport schedule data accuracy, Open Trip Planner routing methods and Open Street Map accuracy. The location of the population-weighted centroid can also influence the validity of the isochrones, when this falls on land which is not possible or is difficult to traverse (e.g., private land and very remote locations).The Northern Ireland public transport data were collated from several files, and as such required additional pre-processing. Location data are missing for two bus stops. Some services run by local public transport providers may also be missing. However, the missing data should have limited impact on the isochrone output. Due to the availability of Northern Ireland public transport data, the isochrones for Northern Ireland were calculated on a comparable but slight later date of 6th December 2022. Any potential future releases are likely to contained aligned dates between all four regions of the UK.In cases where isochrones are not calculable from the population-weighted centroid, or when the calculated isochrones are unrealistically small, the nearest Open Street Map ‘highway’ node is used as an alternative starting point. If this then fails to yield a result, the nearest public transport stop is used as the isochrone origin. If this also fails to yield a result, the geometry will be ‘None’ and the ‘iso_hectar’ will be set to zero. The following information shows a further breakdown of the isochrone types for the UK as a whole:from_centroid: 99.8844%from_node: 0.0332%from_stop: 0.0734%no_trip_found: 0.0090%The term ‘unrealistically small’ in the point above refers to outlier isochrones with a significantly smaller area when compared with both their neighbouring Output/Small Areas and the entire regional distribution. These reflect a very small fraction of circumstances whereby the isochrone extent was impacted by the centroid location and/or how Open Trip Planner handled them (e.g. remote location, private roads and/or no means of traversing the land). Analysis showed these outliers were consistently below 100 hectares for 60-minute isochrones. Therefore, In these cases, the isochrone point of origin was adjusted to the nearest node or stop, as outlined above.During the quality assurance checks, the extent of the isochrones was observed to be in good agreement with other routing software and within the limitations stated within this section. Additionally, the use of nearest node, nearest stop, and correction of ‘unrealistically small areas’ was implemented in a small fraction of cases only. This culminates in no data being available for 8 out of 239,768 Output/Small Areas.Data is only available in ESRI shape file format (.zip) at this release.https://www.openstreetmap.org/copyright

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in London, Dubai, San Francisco, Tokyo, Sydney, Miami, and Toronto in 2023. The data is owned by Airbtics.

Airbtics is a short-term rental data & analytics company monitoring 20 million listings from various short-term rental booking sites.

Content

This data file includes all needed information to find out more about listings, hosts, geographical availability, necessary metrics, such as last twelve months occupancy rate, daily rate and revenue, to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website. The data was processed by Airbtics.

Inspiration

How much does a typical 2-bedroom Airbnb listing make compared to a 3-bedroom in London? What is the average occupancy rate of Airbnb listings in London?

To find more granular data in other cities, visit Airbtics.

The London Streets Traffic Control Centre records and monitors accidents, incidents, road works and public events, which are likely to the impact the normal flow of traffic on London's busiest roads.

The Live Traffic Disruptions XML feed contains information about the location, nature, impact and timing of a range of disruptions being monitored by TfL's 24/7 traffic control centre. The feed comes direct from the control centre's information database and is updated every five minutes.

The feed contains a date and time stamp which should be used to check that the information is up-to-date and be displayed when publishing the information.

Some ideas...

• Each disruption event contains location coordinates, so any website offering travel information for customers could display the data, filtered for their geographic area of interest, on a map
• The information could be combined with TfL's Traffic Camera and Roadside Message sign data feeds to provide a complete picture of traffic along a route or in an area
• The data could be fed into an online road journey planner or mobile navigation system

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales in employment the week before the census by method used to travel to work (2001 specification) and by industry. The estimates are as at Census Day, 21 March 2021.

_As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Due to methodological changes the ‘mainly work at or from home: any workplace type’ category has a population of zero. Please use the transport_to_workplace_12a classification instead. Read more about this quality notice._

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Method used to travel to workplace

A person's place of work and their method of travel to work. This is the 2001 method of producing travel to work variables.

"Work mainly from home" applies to someone who indicated their place of work as their home address and travelled to work by driving a car or van, for example visiting clients.

Industry (current)

Classifies people aged 16 years and over who were in employment between 15 March and 21 March 2021 by the Standard Industrial Classification (SIC) code that represents their current industry or business.

The SIC code is assigned based on the information provided about a firm or organisation’s main activity.

Barnet is in the process of developing a transport strategy to understand and improve the way individuals travel across the borough. With population expected to reach 400,000 by 2020 Barnet is now London’s most populous borough. The growth in Barnet’s population will change our existing communities, attracting a younger and more diverse population, in addition to this, the increase in population will have a significant impact on the way people travel, which is why Barnet is planning to implement a strategy to manage this and make journeys for these people easier. The strategy will outline the Council’s commitment to improving transport options for all of our residents. This will involve considering what our appropriate “mix” of future travel modes should be and how we should be investing in various travel modes in order to arrive at a comprehensive choice of travel options for residents that effectively integrate with one another. It will also provide a high level blueprint to move forward and meet new and emerging challenges as well as providing a local application of the Mayor’s Transport Strategy goals. As part of this strategy an online library has been developed to provide a resource to reinforce decision making and make the strategy more transparent to stake holders. Barnet is in the process of developing a transport strategy to understand and improve the way individuals travel across the borough. With population expected to reach 400,000 by 2020 Barnet is now London’s most populous borough. The growth in Barnet’s population will change our existing communities, attracting a younger and more diverse population, in addition to this, the increase in population will have a significant impact on the way people travel, which is why Barnet is planning to implement a strategy to manage this and make journeys for these people easier. The strategy will outline the Council’s commitment to improving transport options for all of our residents. This will involve considering what our appropriate “mix” of future travel modes should be and how we should be investing in various travel modes in order to arrive at a comprehensive choice of travel options for residents that effectively integrate with one another. It will also provide a high level blueprint to move forward and meet new and emerging challenges as well as providing a local application of the Mayor’s Transport Strategy goals. As part of this strategy an online library has been developed to provide a resource to reinforce decision making and make the strategy more transparent to stake holders.