The IUC ADC became the official source of IUC statistics in April 2021, when the NHS 111 Minimum Dataset (NHS 111 MDS) was merged into a revised version of the IUC ADC. Since then, a provisional subset of the IUC ADC data is published in the month after the collection end date (eg, April data published in May), with the complete monthly IUC ADC published as Official Statistics the following month (eg, April data published in June). The IUC ADC specification is reviewed and updated annually which means not all data items will be directly comparable with the same data items collected in the previous year. The IUC ADC is used to monitor the IUC KPIs. This data is published on the NHS England website. Please follow the link below.

Party identification has been studied extensively using both individual- and aggregate-level data. This paper attempts to formulate a statistical model that can account for the range of empirical generalizations that have emerged from aggregate time series and panel surveys. Using Monte Carlo simulation, we show that only certain types of data generation processes can account for these empirical regularities. Deciding which of the remaining types best explains the data means investigating the ways in which individual-level partisanship behaves over time. Partisanship at the aggregate level tends to be highly autocorrelated, reequilibrating slowly in the wake of each perturbation. Working downward from the analysis of aggregate data, previous researchers argued that aggregate partisanship is fractionally integrated and contended that dynamics at the individual level are therefore heterogeneous. Using data from three panel surveys, we present the first direct assessment of individual-level dynamics. We also investigate the hypothesis that these dynamics vary among individuals, a claim that motivates much recent work on fractionally integrated time series. The model that best explains the observed characteristics of party identification is one in which individuals respond in similar ways to external shocks, reequilibrate rapidly thereafter, and seldom change their equilibrium level of partisan attachment.

This Excel based tool was developed to analyze means-end chain data. The tool consists of a user manual, a data input file to correctly organise your MEC data, a calculator file to analyse your data, and instructional videos. The purpose of this tool is to aggregate laddering data into hierarchical value maps showing means-end chains. The summarized results consist of (1) a summary overview, (2) a matrix, and (3) output for copy/pasting into NodeXL to generate hierarchal value maps (HVMs). To use this tool, you must have collected data via laddering interviews. Ladders are codes linked together consisting of attributes, consequences and values (ACVs).

Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data was reported at 10,590.851 RON mn in Sep 2018. This records a decrease from the previous number of 10,869.273 RON mn for Aug 2018. Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data is updated monthly, averaging 3,905.093 RON mn from Jan 2007 (Median) to Sep 2018, with 141 observations. The data reached an all-time high of 11,274.998 RON mn in Jan 2018 and a record low of 2,477.494 RON mn in Feb 2007. Romania Other MFIs: Assets: Aggregate: Domestic: Cash & Other Payment Means data remains active status in CEIC and is reported by National Bank of Romania. The data is categorized under Global Database’s Romania – Table RO.KB017: Balance Sheet: Other Monetary Financial Institutions.

Asylum applicant means a person having submitted an application for international protection or having been included in such application as a family member during the reference period. New asylum applicant means a person having submitted an application for international protection for the first time

Context

This dataset is the result of my study on web-scraping of English Wikipedia in R and my tests on regression and classification modelization in R.

Content

The content is create by reading the appropriate articles in English Wikipedia about Italian cities: I did'nt run NPL analisys but only the table with the data and I ranked every city from 0 to N in every aspect. About the values, 0 means "*the city is not ranked in this aspect*" and N means "*the city is at first place, in descending order of importance, in this aspect* ". If there's no ranking in a particular aspect (for example, the only existence of the airports/harbours with no additional data about the traffic or the size), then 0 means "*no existence*" and N means "*there are N airports/harbours*". The only not-numeric column is the column with the name of the cities in English form, except some exceptions (for example, "*Bra (CN)* " because of simplicity.

Acknowledgements

I acknowledge the Wikimedia Foundation for his work, his mission and to make available the cover image of this dataset, (please read the article "The Ideal city (painting)") . I acknowledge too StackOverflow and Cross-Validated to be the most important focus of technical knowledge in the world, all the people in Kaggle for the suggestions.

Inspiration

As a beginner in data analisys and modelization (Ok, I passed the exam of statistics in Politecnico di Milano (Italy), but there are more than 10 years that I don't work in this topic and my memory is getting old ^_^) I worked more on data clean, dataset building and building the simplest modelization.

You can use this datase to realize which city is good to live or to expand this to add some other data from Wikipedia (not only reading the tables but too to read the text adn extrapolate the data from the meaningless text.)

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

This session will focus on the baseline of skills that Data Liberation Initiative (DLI) Contacts should have and the corresponding training to achieve these skills. Introducing newcomers to the language of statistics and data is one of the important tasks of the orientation. Acquiring a technical language often poses a barrier to newcomers. To overcome this hurdle, newcomers must grasp both the meaning of new concepts and its abbreviated language of acronyms. Should we expect the orientation to offer all of the baseline skills or is other instruction needed? Do different local environments result in varying uses of DLI resources? Are the same skills needed among differing environments? How much attention should be paid during the orientation to different models of data service? For example, should the implications of buying services from elsewhere (e.g., Sherlock, IDLS, CHASS, Queen’s, etc.) be covered? What kind of distinctions need to be made for the levels of support for instructional and research uses of data? What about the reference uses of data, that is, using data to answer reference questions? Are there additional skills required of those supporting DLI data for research and reference uses? If there are, what are they and how should they be introduced?

This dataset shows the location of Marine Aggregate Agreements issued on Crown Estate owned seabed. Natural Resources Wales (NRW) has access to a number of Crown Estate datasets associated with the offshore aggregates industry. NRW can use this dataset under the terms and conditions of a bespoke Crown Estate Licence called 'the Data Means Aggregate Production Licence'. The data can be used for internal business use only.

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Romania Other MFIs: Assets: Aggregate: Foreign: Cash & Other Payment Means data was reported at 4,748.161 RON mn in Jun 2018. This records an increase from the previous number of 4,439.754 RON mn for May 2018. Romania Other MFIs: Assets: Aggregate: Foreign: Cash & Other Payment Means data is updated monthly, averaging 1,469.158 RON mn from Jan 2007 (Median) to Jun 2018, with 138 observations. The data reached an all-time high of 4,748.161 RON mn in Jun 2018 and a record low of 763.879 RON mn in Feb 2007. Romania Other MFIs: Assets: Aggregate: Foreign: Cash & Other Payment Means data remains active status in CEIC and is reported by National Bank of Romania. The data is categorized under Global Database’s Romania – Table RO.KB018: Balance Sheet: Other Monetary Financial Institutions.

The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

Abstract copyright UK Data Service and data collection copyright owner.The UK censuses took place on 29th April 2001. They were run by the Northern Ireland Statistics & Research Agency (NISRA), General Register Office for Scotland (GROS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics, and underpin funding allocation to provide public services. The aggregate statistics produced as outputs from UK censuses provide information on a wide range of demographic and socio-economic characteristics of the population of the United Kingdom. They are predominantly a collection of aggregated, or summary counts of the numbers of people, families or households resident in specific geographical areas or ‘zones’ possessing particular characteristics, or combinations of characteristics drawn from the themes of population, people and places, families, ethnicity and religion, health, work, and housing. Aggregate statistics are available for the full range of geographies employed within the census, from the smallest (output areas with an average of 150 persons in England and Wales), to national level. For further information about the geographies used in the output of census aggregate statistics, see the section on census geography in the Office for National Statistics’ Beginner’s Guide to UK Geography. Data can be accessed through InFuse. Citation Through InFuse: Office for National Statistics (2011): 2001 Census aggregate data (Edition: May 2011). UK Data Service. DOI: https://doi.org/10.5257/census/aggregate-2001-2 Through Casweb: Office for National Statistics; General Register Office for Scotland; Northern Ireland Statistics and Research Agency (2005): 2001 Census aggregate data (Edition: 2005). UK Data Service. DOI: https://doi.org/10.5257/census/aggregate-2001-1 Main Topics:Accommodation type (brief)Accommodation type (detailed)Adults, Number Employed in HouseholdAdults, Number in HouseholdAgeAge of Family Reference Person (FRP)Age of Household Reference Person (HRP)Age of Students and SchoolchildrenAmenitiesArmed ForcesBath/Shower and Toilet, use ofCare (unpaid), Provision ofCare, Provision ofCarers and their Economic Activity, Number ofCars and vansCentral heatingChildrenChildren, dependentCommunal Establishment ResidentsCommunal establishment, combined type and managementConcealed familiesCountry of birthCountry of Birth (additional categories)Daytime PopulationDwelling TypeEconomic ActivityEconomic Activity of Associated People Resident in HouseholdsEconomic Activity of Full-time studentsEconomic Activity of Household Reference Person (HRP)Ethnic group (England and Wales)Ethnic group (England and Wales) of Household Reference PersonFamily compositionFamily statusFamily typeHealth, GeneralHours workedHousehold compositionHousehold composition (alternative classification)Household dependent childrenHousehold deprivationHousehold Reference Person indicatorHousehold sizeHousehold Space TypeHousehold TypeHouseholds with students away during term-timeIndustryIndustry, formerLimiting long-term illnessLimiting Long-Term Illness (LLTI), Household residents withLimiting long-Term Illness, number of people with in householdLiving arrangementsLiving arrangements of Household Reference Person (HRP)Lowest floor levelMarital statusMigration (armed forces)Migration (Communal establishment)Migration (People)Multiple ethnic identifierOccupancy RatingOccupation (brief)Occupation (detailed)Occupation, formerPensioner householdPeople aged 17 or over in household, Number ofPopulation TypePublic transport users in householdsQualifications (England and Wales)Qualifications, highest level of (England and Wales)Qualifications, professionalReligion (England and Wales)Religion (England and Wales) of Household Reference PersonResident BasisResident TypeRooms in a dwelling, number ofRooms, Number ofRooms, Persons perSexSex of Household Reference Person (HRP)Single Adult HouseholdsSocial Grade of Household Reference Person (HRP), approximatedSocial Grade, approximatedSocio-economic Classification (NS-SeC)Socio-economic Classification (NS-SeC) of Household Reference Person (HRP)Socio-economic Classification (NS-SeC) of Household Reference Person (HRP), Main categories ofStudent accommodation (Standard Output)Student accommodation TypeStudent statusTenureTenure, dwellingTime Since Last WorkedTravel to Work, distanceTravel to work, Means ofTravel to Work, Method of and Number of Employed PeopleWorking ParentsYear last worked

Aggregate means for six traits (milk, fat, and protein yields, somatic cell score, length of productive life, and daughter pregnancy rate) Resources in this dataset:Resource Title: Holstein Milk Yield. File Name: HO_M.csvResource Description: Aggregate means of Holstein predicted breeding values for milk yield and birth datesResource Title: Holstein Fat Yield. File Name: HO_f.csvResource Description: Aggregate means of Holstein predicted breeding values for fat yield and birth datesResource Title: Holstein Protein Yield. File Name: HO_p.csvResource Description: Aggregate means of Holstein predicted breeding values for protein yield and birth datesResource Title: Holstein Somatic Cell Score. File Name: HO_scs.csvResource Description: Aggregate means of Holstein predicted breeding values for somatic cell score and birth datesResource Title: Holstein Productive Life. File Name: HO_pl.csvResource Description: Aggregate means of Holstein predicted breeding values for productive life and birth datesResource Title: Holstein Daughter Pregnancy Rate. File Name: HO_DPR.csvResource Description: Aggregate means of Holstein predicted breeding values for daughter pregnancy rate and birth datesResource Title: Data Dictionary. File Name: data_dictionary.csvResource Description: Defines variables / sub-components with examples as used in column headers. Filenames: Holstein Productive Life: HO_pl.csv Holstein Daughter Pregnancy Rate: HO_DPR.csv Holstein Somatic Cell Score: HO_scs.csv Holstein Protein Yield: HO_p.csv Holstein Fat Yield: HO_f.csv Holstein Milk Yield: HO_M.csv

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 c...

We argue that voice is too simple a concept – there are different types of voice, collective (e.g. voting, group membership) and individual voice (e.g. complaining). We suggest that collective voice is harder to organise because of coordination problems whereas individual voice. We argue that individual voice does not trade-off with exit, collective voice does. Exit takes different forms too: moving providers within jurisdiction, moving jurisdiction, and exit to private services. We suggest there are different relationships between these variables. As a result of these characteristics, we find that many of the arguments both for and againsts choice need to be carefully considered, and we have reviewed these in the contest of the research evidence to show that the arguments for more choice need to be carefully considered and applicable to the context in which they occur. In the empirical part of our project, we test whether the possibility of exit makes voice less likely, which suggests that the greater complexity at the local level and new provision introducing more choice, allowing for greater exit opportunities, should cause the quality of local participation to suffer. We also test whether loyalty in the form of the level of social investment in a community or social capital should increase the probability of voice over exit. We used a panel survey of a representative group of 4000 internet users, asking questions about service satisfaction, use of private services, other exit opportunities, social investment into the local community, and the level of participation, which we repeated a year later. This research applies the ideas expressed in Albert Hirschman’s book, Exit, Voice and Loyalty. He argued that there is a trade-off between exit and voice, which may decrease efficiency because the voice of consumers keeps organisations responsive whereas exit leaves the poor performers behind. In public services, citizens may exit from public provision by moving across jurisdictions and/or moving to other private sector providers. Citizens may use collective voice through: voting and pressure group activity or private voice by personal complaints and comments to public officials. We will test whether the possibility of exit makes voice less likely, which suggests that the greater complexity at the local level and new provision introducing more choice, allowing for greater exit opportunities, should cause the quality of local participation to suffer. We also will test whether loyalty in the form of the level of social investment in a community or social capital should increase the probability of voice over exit. We will use a panel survey of a representative group of 2000 internet users, asking questions about service satisfaction, use of private services, other exit opportunities, social investment into the local community, and the level of participation. Internet survey of a representative sample of users(individuals, internet users) from YouGov's panel. Aggregate data sources from Census, from Local Elections Unit, University of Plymouth, and from political data from local authority websites. Samples: 4067 respondents in Wave 1, 2610 for Wave 2. 741 variables (including 32 variables added from census and local authority data).

This spatial dataset represents areas where resources may be extracted within the limits of the aggregate licence or permit for the associated site. Reporting requirements are optional, which means records will be sporadic and limited to certain areas of the province.

Additional details related to aggregates in Ontario are available in related data classes as well as online using the interactive Pits and Quarries map.

Additional Documentation

  Aggregate Extraction Area - Data Description (PDF)
  Aggregate Extraction Area - Documentation (Word)

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Workers include members of the Armed Forces and civilians who were at work last week..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.