This dataset contains FEMA applicant-level data for the Individuals and Households Program (IHP). All PII information has been removed. The location is represented by county, city, and zip code. This dataset contains Individual Assistance (IA) applications from DR1439 (declared in 2002) to those declared over 30 days ago. The full data set is refreshed on an annual basis and refreshed weekly to update disasters declared in the last 18 months. This dataset includes all major disasters and includes only valid registrants (applied in a declared county, within the registration period, having damage due to the incident and damage within the incident period). Information about individual data elements and descriptions are listed in the metadata information within the dataset.rnValid registrants may be eligible for IA assistance, which is intended to meet basic needs and supplement disaster recovery efforts. IA assistance is not intended to return disaster-damaged property to its pre-disaster condition. Disaster damage to secondary or vacation homes does not qualify for IHP assistance.rnData comes from FEMA's National Emergency Management Information System (NEMIS) with raw, unedited, self-reported content and subject to a small percentage of human error.rnAny financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and application of business rules, this financial information may differ slightly from official publication on public websites such as usaspending.gov. This dataset is not intended to be used for any official federal reporting. rnCitation: The Agency’s preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnDue to the size of this file, tools other than a spreadsheet may be required to analyze, visualize, and manipulate the data. MS Excel will not be able to process files this large without data loss. It is recommended that a database (e.g., MS Access, MySQL, PostgreSQL, etc.) be used to store and manipulate data. Other programming tools such as R, Apache Spark, and Python can also be used to analyze and visualize data. Further, basic Linux/Unix tools can be used to manipulate, search, and modify large files.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.rnThis dataset is scheduled to be superceded by Valid Registrations Version 2 by early CY 2024.

New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data marked with n/a over the period 1969 and 1973 are not available. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Figure changed on the 27/6/16 as revised data received from the Local authority Includes houses and apartments, measured in €

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Department of Conservation (DOC) - recreation track lines (approx. centreline). Dataset shows all tracks. If you intend to walk a track, please confirm with your local office or the DOC website that the track isn't under a temporary or more permanent closure before embarking.Detailed characteristics about each track are held in the ‘CharName’ and ‘CharValue’ fields in the attribute table.Each ‘CharName’ field contains the name of a characteristic, and each ‘CharValue’ field to the right of this contains the value related to this characteristic.*DISCLAIMER1. DOC makes no express or implied warranties as to the accuracy or completeness of the data or information, nor its suitability for any purpose. Errors are inevitably part of any database, and can arise by a number of means, from errors during field data collection, to errors during data entry.2. DOC makes no warranties or representations as to possible infringement upon copyrights or other intellectual property rights of others in the data or information.3. DOC will not accept liability for any direct, indirect, special or consequential damages, losses or expenses howsoever arising and relating to use, or lack of use, of the data or information supplied.GUIDELINES FOR THE USE OF THE INFORMATION4. Care should be taken in deriving conclusions from any data or information supplied.5. Any use of the data or information supplied should state when the data or information was acquired and that it may now be out-of-date.COPYRIGHT OBLIGATIONS6. All proprietary rights to the intellectual property in the data or information remain with the Crown as its sole property.7. Modification of the data and information or the addition of the information does not confer copyright or any other form of property of the original material to a user.8. All maps or reports that are derived from the data or information must acknowledge the Crown copyright, in the following way: Crown Copyright: Department of Conservation Te Papa Atawhai [year].9. This information resource may be passed onto another party, in either hard copy or electronic form. If a user does this, then it is recommended that they also supply this metadata record with the information resource.LICENCE***This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.

This dataset contains the properties that were mitigated by projects funded under the Hazard Mitigation Assistance (HMA) grant programs. FEMA administers three programs that provide funding for eligible mitigation planning and projects to reduce disaster losses and protect life and property from future disaster damages. The three programs are the Hazard Mitigation Grant Program (HMGP), Flood Mitigation Assistance (FMA) grant program, and Pre-Disaster Mitigation (PDM) grant program. This dataset also contains data from the HMA grant programs that were eliminated by the Biggert Water Flood Insurance Reform Act of 2012 (BW-12): Repetitive Flood Claims (RFC) grant program and Severe Repetitive Loss (SRL) grant program. For more information on the Hazard Mitigation Assistance grant programs, please visit: https://www.fema.gov/grants/mitigation.rnrnThe dataset contains properties by project identifier, city, zip code, state and region and does not contain any Personally Identifiable Information (PII). The mitigated property dataset can be joined to the OpenFEMA Hazard Mitigation Assistance Funded Project dataset by the Project Identifier field. Note, not all projects in the Hazard Mitigation Assistance Funded Project dataset will have mitigated properties (e.g., Planning and Management Cost projects). In some cases data was not provided by the subgrantee (sub-recipient), grantee (recipient) and/or entered into the FEMA mitigation grant systems. The information is likely available as part of the paper file which is considered the file of record.rnrnThis is raw, unedited data from FEMA's mitigation grant systems (NEMIS-MT and e-Grants) and as such is subject to a small percentage of human error. The financial information is derived from FEMA's mitigation grant systems and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and how business rules are applied, this financial information may differ slightly from official publication on public websites such as www.usaspending.gov; this dataset is not intended to be used for any official federal financial reporting.rnrnMissing values - In some cases data was not provided by the subgrantee (subrecipient), grantee (recipient) and/or entered into the FEMA mitigation grant systems. The information is likely available as part of the paper file which is considered the file of record.rnrnA newer version of this OpenFEMA data set has been released. This older dataset version will no longer be updated and will be archived by the end of April 2020. The following page details the latest version of this data set:https://www.fema.gov/openfema-data-page/hazard-mitigation-assistance-mitigated-properties-v2. CSV and JSON Files can be downloaded from the 'Full Data' section.rnrnTo access the dataset through an API endpoint, visit the 'API Endpoint' section of the above page. Accessing data in this fashion permits data filtering, sorting, and field selection. The OpenFEMA API Documentation page provides information on API usage. rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's Hazard Mitigation Assistance grant program data and Open government program, please contact the OpenFEMA team via email OpenFEMA@fema.dhs.gov and FEMA-HMAAnalytics@fema.dhs.gov.

Property Market Insights from Zoopla Live (Aggregated):

Discover valuable property market insights with our data product sourced from Zoopla Live, one of the UK's premier aggregators of property listings data. Gain access to a comprehensive dataset containing information on 27,000,000 homes, up to 1,000,000 property listings,.

The Urban Big Data Centre (UBDC) systematially collects daily property listings from Zoopla across the entire UK since 2017; and each year, we process and consolidate the collected data into yearly aggregated and harmonized 'analysis-ready' datasets.

Researchers can utilize this dataset to unlock meaningful insights into the property market.

Please note that additional daily data from Zoopla can be made available upon request if the provided aggregated product does not meet specific research requirements. Additionally, it's essential to be aware that each Zoopla year spans 18 months, from the 1st of Oct of the previous year until the 31st of March of the following year.

UBDC’s Zoopla data collection is a dataset that covers housing data since 2017, covering the area of Great Britain. UBDC has an agreement with Zoopla and has access to current property listings via Application Programming Interface (API). We have been collecting the listings since August 2016.

UBDC can provide Live Collected Aggregated Data: Aggregated (yearly) data made from Live Collected data. The Zoopla year is +/- 3 months for each year(= zoopla year 18 months) The collection requires unique property id in the API request.

We have also set up an email discussion list on housing data and related issues that you can join via the JISCMail website:

https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=UBDC-HOUSING-DATA

Note: In accessing this data, you agree that any downloading of content is for non-commercial reference only. No part of these materials may be used for any other purpose or reproduced or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior consent of the University of Glasgow.

Data is Zoopla Property Group PLC, © 2023, processed by Urban Big Data Centre, University of Glasgow

Details of the data available via Zoopla’s Application Programming Interface (API) can be found at

http://developer.zoopla.com/

WWNP Floodplain Reconnection Potential is our best estimate of locations where it may be possible to establish reconnection between a watercourse and its natural floodplain, especially during high flows. The dataset is designed to support signposting of areas where there is currently poor connectivity such that flood waters are constrained to the channel and flood waves may therefore propagate downstream rapidly. The dataset is based upon the Risk of Flooding from Rivers and Sea probability maps, and identifies areas of low and very low probability that are close to a watercourse, but which do not contain residential property or key services.The areas may contain non-residential property so it is important to consider this and recent buildings or defences when considering floodplain reconnection. Locations identified may have more recent building or land use than available data indicates. It is important to note that land ownership and change to flood risk have not been considered, and it may be necessary to model the impacts of significant reconnection.Areas of low or very low probability of fluvial flooding were identified from the Risk of Flooding from Rivers and Sea dataset. The Detailed River Network (2013) was used to derive the proximity of the watercourses to the floodplain, but not displayed due to data licensing restrictions. The National Receptor Dataset (2014) property points were used to screen out areas with residential property and key services. This dataset is not open data, so is also not displayed in the mapping work

This dataset has been produced as part of the Mapping Potential for Working with Natural Processes research project (SC150005). The project created a toolbox of mapped data and methods which enable operational staff in England to identify potential locations for Working with Natural Processes (WWNP). Data has been produced for each intervention covered by the project. The final outputs include the following datasets: • Floodplain Woodland Planting Potential • Riparian Woodland Planting Potential • Wider Catchment Woodland • Floodplain Reconnection Potential • Runoff Attenuation Features 3.3% AEP • Runoff Attenuation Features 1% AEP • Woodland Constraints WWNP Floodplain Reconnection Potential is our best estimate of locations where it may be possible to establish reconnection between a watercourse and its natural floodplain, especially during high flows. The dataset is designed to support signposting of areas where there is currently poor connectivity such that flood waters are constrained to the channel and flood waves may therefore propagate downstream rapidly. The dataset is based upon the Risk of Flooding from Rivers and Sea probability maps, and identifies areas of low and very low probability that are close to a watercourse, but which do not contain residential property or key services. The areas may contain non-residential property so it is important to consider this and recent buildings or defences when considering floodplain reconnection. Locations identified may have more recent building or land use than available data indicates. It is important to note that land ownership and change to flood risk have not been considered, and it may be necessary to model the impacts of significant reconnection. Further information on the Working with Natural Processes project, including a mapping user guide, can be found in the reports published here: https://www.gov.uk/government/publications/working-with-natural-processes-to-reduce-flood-risk Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in €

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 50.75% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Washington Court House is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Washington Court House 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 Washington Court House Population by Gender. You can refer the same here

What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index.

About this dataset

Context

The dataset tabulates the Red House town 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 Red House town. The dataset can be utilized to understand the population distribution of Red House town by age. For example, using this dataset, we can identify the largest age group in Red House town.

Key observations

The largest age group in Red House, New York was for the group of age 40 to 44 years years with a population of 10 (38.46%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Red House, New York was the 5 to 9 years years with a population of 0 (0%). 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 Red House town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Red House town 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 Red House town Population by Age. You can refer the same here

About this dataset

Context

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

Key observations

There is a majority of male population, with 53.85% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 House is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of House 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 House Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the House 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 House. The dataset can be utilized to understand the population distribution of House by age. For example, using this dataset, we can identify the largest age group in House.

Key observations

The largest age group in House, NM was for the group of age 60 to 64 years years with a population of 16 (28.07%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in House, NM was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 House is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of House 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 House Population by Age. You can refer the same here

Development of the National Register for Social Housing (NROSH) was started by the Department for Communities and Local Government (DCLG) in 2004. NROSH aimed to be a database of all social housing properties in England, with a range of details captured on each property. NROSH was transferred to the Tenant Services Authority, the social housing regulator, in April 2010 and was discontinued in May 2011. Ownership of the latest NROSH dataset passed from the TSA to the Homes and Communities Agency (HCA) when responsibility for social housing regulation passed to the Regulation Committee of the HCA in April 2012.

In addition to being out of date, the records submitted by social landlords to NROSH are of varying quantity and quality with many incomplete, inaccurate or missing records. The database may also contain a number of duplicate entries.

Two datasets are available. One is the latest NROSH database held by the HCA as at May 2011. This release contains a large subset of the full NROSH dataset (48 from 201 fields in total; for 4,826,417 unique property records). The data in this release does not include those fields where data could enable specific identification of vulnerable people or other sensitive personal data. It also excludes fields where a minimum completion threshold is not met (generally fields where less than 25% of records have data). There are still issues of quality, incomplete data, and potential duplication of records in the data that accompanies this release that HCA is not able to resolve.

Additional information, including data that falls below the minimum quality thresholds for this release, may be requested from the HCA (Referrals & Regulatory Enquiries Team, mail@homesandcommunities.co.uk).

The 48 fields included in this release are summarised and described in the two tables accompanying this metadata. The data is contained in five compressed single CSV files: NROSH Data Extract Part 1; - 2; - 3; -4 and -5. Due to the large volume of records, analysis will require database software (MS Excel will not support analysis).

Also available is a snapshot of the NROSH database held by DCLG as at March 2010. The data is that which was reported by social landlords in line with the system specifications and includes a selected set of fields on property address, type of accommodation, form of structure, number of rooms and bedspaces are included.

Department of Conservation (DOC) - Campsites. Dataset shows all campsites. If you intend to stay in a campsite, please confirm with your local office or the DOC website that it is available and not under a temporary or more permanent closure before departing. Please note some campsites require advance booking, contact your local office or visit the DOC Website for more information. Refreshed weekly and reflects the content on the website.*LICENCEThis work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.DISCLAIMER 1. DOC makes no express or implied warranties as to the accuracy or completeness of the data or information, nor its suitability for any purpose. Errors are inevitably part of any database, and can arise by a number of means, from errors during field data collection, to errors during data entry. 2. DOC makes no warranties or representations as to possible infringement upon copyrights or other intellectual property rights of others in the data or information. 3. DOC will not accept liability for any direct, indirect, special or consequential damages, losses or expenses howsoever arising and relating to use, or lack of use, of the data or information supplied.GUIDELINES FOR THE USE OF THE INFORMATION 4. Care should be taken in deriving conclusions from any data or information supplied. 5. Any use of the data or information supplied should state when the data or information was acquired and that it may now be out-of-date.COPYRIGHT OBLIGATIONS*** 6. All proprietary rights to the intellectual property in the data or information remain with the Crown as its sole property. 7. Modification of the data and information or the addition of the information does not confer copyright or any other form of property of the original material to a user. 8. All maps or reports that are derived from the data or information must acknowledge the Crown copyright, in the following way: Crown Copyright: Department of Conservation Te Papa Atawhai [year]. 9. This information resource may be passed onto another party, in either hard copy or electronic form. If a user does this, then it is recommended that they also supply this metadata record with the information resource

Law Enforcement Locations Any location where sworn officers of a law enforcement agency are regularly based or stationed. Law Enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies. Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police). In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state. Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS deleted these locations as we became aware of them, but some may still exist in this dataset. Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes are included due to the fact that the New Mexico Mounted Police work out of their homes. TGS has made a concerted effort to include all local police; county sheriffs; state police and/or highway patrol; Bureau of Indian Affairs; Bureau of Land Management; Bureau of Reclamation; U.S. Park Police; Bureau of Alcohol, Tobacco, Firearms, and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection. This dataset is comprised completely of license free data. FBI entities are intended to be excluded from this dataset, but a few may be included. The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. With the merge of the Law Enforcement and the Correctional Institutions datasets, the NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer). Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 08/14/2006 and the newest record dates from 10/23/2009

******Disclaimer: This data should not be relied upon for title examination or real estate closing requirements as deferral and tax relief programs may impact the amount due. This data does not include nuisance abatement liens.

This dataset includes data on all real estate parcels in the city of Norfolk that are at least one quarter behind on taxes. Payments are processed as received requiring frequent updates to the data for accuracy. City deferral and senior tax relief programs may impact r-portable data. The tax rate, penalty rate and interest rate are prescribed by city ordinance.

To view the most updated version of the dataset, please click here: https://data.norfolk.gov/Government/Delinquent-Property-Taxes/7qie-z5gv/about_data