About this dataset

Context

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

Key observations

Largest age group (population): Male # 40-44 years (5) | Female # 40-44 years (5). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Red House town Population by Gender. You can refer the same here

This dataset is OBSOLETE as of 12/3/2024 and will be removed from ArcGIS Online on 12/3/2025.An updated version of this dataset is available at Land Use FY2024.This data set derives from several sources, and is updated annually with data current through July 1 of the reported year. The primary source is a data dump from the VISION assessing data system, which provided data up to date as of January 1, 2012, and is supplemented by information from subsequent building permits and Development Logs. (Use codes provided by this system combine aspects of land use, tax status, and condominium status. In an effort to clarify land use type the data has been cleaned and subdivided to break the original use code into several different fields.) The data set has further been supplemented and updated with development information provided by building permits issued by the Inspectional Services Department and from data found in the Development Log publication. Information from these sources is added to the data set periodically. Land use status is up to date as of the Last Modified date.Differences From “Official” Parcel LayerThe Cambridge GIS system maintains a separate layer of land parcels reflecting up to date subdivision and ownership. The parcel data associated with the Land Use Data set differs from the “official” parcel layer in a number of cases. For that reason this separate parcel layer is provided to work with land use data in a GIS environment. See the Assessing Department’s Parcel layer for the most up-to-date land parcel boundaries.Table of Land Use CodesThe following table lists all land use code found in the data layer:Land Use CodeLand Use DescriptionCategory0101MXD SNGL-FAM-REMixed Use Residential0104MXD TWO-FAM-RESMixed Use Residential0105MXD THREE-FM-REMixed Use Residential0111MXD 4-8-UNIT-APMixed Use Residential0112MXD >8-UNIT-APTMixed Use Residential0121MXD BOARDING-HSMixed Use Residential013MULTIUSE-RESMixed Use Residential031MULTIUSE-COMMixed Use Commercial0340MXD GEN-OFFICEMixed Use Commercial041MULTIUSE-INDMixed Use Industrial0942Higher Ed and Comm MixedMixed Use Education101SNGL-FAM-RESResidential1014SINGLE FAM W/AUResidential104TWO-FAM-RESResidential105THREE-FM-RESResidential106RES-LAND-IMPTransportation1067RES-COV-PKGTransportation1114-8-UNIT-APTResidential112>8-UNIT-APTResidential113ASSISTED-LIVAssisted Living/Boarding House121BOARDING-HSEAssisted Living/Boarding House130RES-DEV-LANDVacant Residential131RES-PDV-LANDVacant Residential132RES-UDV-LANDVacant Residential1322RES-UDV-PARK (OS) LNVacant Residential140CHILD-CARECommercial300HOTELCommercial302INN-RESORTCommercial304NURSING-HOMEHealth316WAREHOUSECommercial323SH-CNTR/MALLCommercial324SUPERMARKETCommercial325RETAIL-STORECommercial326EATING-ESTBLCommercial327RETAIL-CONDOCommercial330AUTO-SALESCommercial331AUTO-SUPPLYCommercial332AUTO-REPAIRCommercial334GAS-STATIONCommercialLand Use CodeLand Use DescriptionCategory335CAR-WASHCommercial336PARKING-GARTransportation337PARKING-LOTTransportation340GEN-OFFICEOffice341BANKCommercial342MEDICAL-OFFCHealth343OFFICE-CONDOOffice345RETAIL-OFFICOffice346INV-OFFICEOffice353FRAT-ORGANIZCommercial362THEATRECommercial370BOWLING-ALLYCommercial375TENNIS-CLUBCommercial390COM-DEV-LANDVacant Commercial391COM-PDV-LANDVacant Commercial392COM-UDV-LANDVacant Commercial3922CRMCL REC LNDVacant Commercial400MANUFACTURNGIndustrial401WAREHOUSEIndustrial404RES-&-DEV-FCOffice/R&D406HIGH-TECHOffice/R&D407CLEAN-MANUFIndustrial409INDUST-CONDOIndustrial413RESRCH IND CNDIndustrial422ELEC GEN PLANTUtility424PUB UTIL REGUtility428GAS-CONTROLUtility430TELE-EXCH-STAUtility440IND-DEV-LANDVacant Industrial442IND-UDV-LANDVacant Industrial920ParklandsPublic Open Space930Government OperationsGovernment Operations934Public SchoolsEducation940Private Pre & Elem SchoolEducation941Private Secondary SchoolEducation942Private CollegeHigher Education9421Private College Res UnitsEducation Residential943Other Educ & Research OrgHigher EducationLand Use CodeLand Use DescriptionCategory953CemeteriesCemetery955Hospitals & Medical OfficHealth956MuseumsHigher Education957Charitable ServicesCharitable/Religious960ReligiousCharitable/Religious971Water UtilityUtility972Road Right of WayTransportation975MBTA/RailroadTransportation9751MBTA/RailroadTransportation995Private Open SpacePrivately-Owned Open SpaceExplore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription ML type: Stringwidth: 16precision: 0 Map-Lot: This a unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

MAP type: Stringwidth: 5precision: 0 This Map portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

LOT type: Stringwidth: 5precision: 0 This is the Lot portion of the unique parcel identifier found in the deed and used by the Assessing data system. In a few cases, where parcels have been subdivided subsequent to January 1, 2012, a placeholder Map-Lot number is assigned that differs from that used elsewhere.

Location type: Stringwidth: 254precision: 0 In the great majority of cases this is the street address of the parcel as it is recorded in the Registry of Deed record. In instances where edits were made to the base parcel layer the best address available at the time is employed.

LandArea type: Doublewidth: 8precision: 15

LUCode type: Stringwidth: 254precision: 0 The four digit text string in this field indicates the primary usage of a parcel. While the codes are based on the standard Massachusetts assessing land use classification system, they differ in a number of cases; the coding system used here is unique to this data set. Note that other minor uses may occur on a property and, in some cases, tenants may introduce additional uses not reflected here (eg, office space used as a medical office, home based businesses).

LUDesc type: Stringwidth: 254precision: 0 The short description gives more detail about the specific use indicated by the Land Use Code. Most descriptions are taken from the standard Massachusetts assessing land use classification system.

Category type: Stringwidth: 254precision: 0 This broader grouping of land uses can be used to map land use data. You can find the land use data mapped at: https://www.cambridgema.gov/CDD/factsandmaps/mapgalleries/othermaps

ExistUnits type: Doublewidth: 8precision: 15 This value indicates the number of existing residential units as of July 1 of the reported year. A residential unit may be a house, an apartment, a mobile home, a group of rooms or a single room that is occupied (or, if vacant, intended for occupancy) as separate living quarters. This includes units found in apartment style graduate student housing residences and rooms in assisted living facilities and boarding houses are treated as also housing units. The unit count does not include college or graduate student dormitories, nursing home rooms, group homes, or other group quarters living arrangements.

MixedUseTy type: Stringwidth: 254precision: 0 Two flags are used for this field. “Groundfloor” indicates that a commercial use is found on the ground floor of the primary building, and upper floors are used for residential purposes. “Mixed” indicates that two or more uses are found throughout the structure or multiple structures on the parcel, one of which is residential.

GQLodgingH type: Stringwidth: 254precision: 0 A value of “Yes” indicates that the primary use of the property is as a group quarters living arrangement. Group quarters are a place where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents. Group quarters include such places as college residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, and workers’ dormitories.

Most university dormitories are included under the broader higher education land use code, as most dormitories are included in the larger parcels comprising the bulk of higher education campuses.

GradStuden type: Stringwidth: 254precision: 0 A value of “Yes” indicates the parcel is used to house graduate students in apartment style units. Graduate student dormitories are treated as a higher education land use.

CondoFlag type: Stringwidth: 254precision: 0 “Yes” indicates that the parcel is owned as a condominium. Condo properties can include one or more uses, including residential, commercial, and parking. The great majority of such properties in Cambridge are residential only.

TaxStatus type: Stringwidth: 254precision: 0 A value indicates that the parcel is not subject to local property taxes. The following general rules are employed to assign properties to subcategories, though special situations exist in a number of cases.

o Authority: Properties owned the Cambridge Redevelopment Authority and Cambridge Housing Authority. o City: Properties owned by the City of Cambridge or cemetery land owned by the Town of Belmont. o Educ: Includes properties used for education purposes, ranging from pre-schools to university research facilities. (More detail about the level of education can be found using the Land Use Code.) o Federal: Properties owned by the federal government, including the Post Office. Certain properties with assessing data indicating Cambridge Redevelopment Authority ownership are in fact owned by the federal government as part of the Volpe Transportation Research Center and are so treated here. o Other: Nontaxable properties owned by a nonprofit organization and not

SAFI (Studying African Farmer-Led Irrigation) is a currently running project which is looking at farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members.This is a teaching version of the collected data, it is not the full dataset. The survey is split into several sections:A – General questions about when and where the survey was conducted.B - Information about the household and how long they have been living in the areaC – Details about the accommodation and other buildings on the farmD – Details about the different plots of land they grow crops onE – Details about how they irrigate the land and availability of waterF – Financial details including assets owned and sources of incomeG – Details of Financial hardshipsX – Information collected directly from the smartphone (GPS) or automatically included in the form (instanceID)key_id Added to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)A01_interview_date, Date of InterviewA03_quest_no, Questionnaire numberA04_start, Timestamp of start of InterviewA05_end, Timestamp of end of InterviewA06_province, Province nameA07_district, District nameA08_ward, Ward nameA09_village, Village nameA11_years_farm, Number of years the household have been farming in this areaA12_agr_assoc, Does the head of the household belong to an agricultural association_note2 Possible form comment relating to the sectionB_no_membrs, How many members of the household?_members_count Internal count of membersB11_remittance_money, Is there any financial assistance from family members not living on the farmB16_years_liv, How many years have you been living in this village or neighbouring village?B17_parents_liv, Did your parents live in this village or neighbouring village?B18_sp_parents_liv, Did your spouse's parents live in this village or neighbouring village?B19_grand_liv, Did your grandparents live in this village or neighbouring village?B20_sp_grand_liv, Did your spouse's grandparents live in this village or neighbouring village?C01_respondent_roof_type, What type of roof does their house have?C02_respondent_wall_type, What type of walls does their house have (from list)C02_respondent_wall_type_other, What type of walls does their house have (not on list)C03_respondent_floor_type, What type of floor does their house have C04_window_type, Does the house have glass in at least one window?C05_buildings_in_compound, How many buildings are in the compound? Do not include stores, toilets or temporary structures.C06_rooms, How many rooms in the main house are used for sleeping?C07_other_buildings, Does the DU own any other buildings other than those on this plotD_no_plots, How many plots were cultivated in the last 12 months?D_plots_count, Internal count of plotsE01_water_use, Do you bring water to your fields, stop water leaving your fields or drain water out of any of your fields?E_no_group_count, How many plots are irrigated?E_yes_group_count, How many plots are not irrigated?E17_no_enough_water, Are there months when you cannot get enough water for your crops? Indicate which months.E18_months_no_water, Please select the monthsE19_period_use, For how long have you been using these methods of watering crops? (years)E20_exper_other, Do you have experience of such methods on other farms?E21_other_meth, Have you used other methods before?E22_res_change, Why did you change the way of watering your crops?E23_memb_assoc, Are you a member of an irrigation association?E24_resp_assoc, Do you have responsibilities in that association?E25_fees_water, Do you pay fees to use water?E26_affect_conflicts, Have you been affected by conflicts with other irrigators in the area ?_note Form comment for sectionF04_need_money, If you started or changed the way you water your crops recently, did you need any money for it?F05_money_source, Where did the money came from? (list)F05_money_source_other, Where did the money came from? (not on list)F06_crops_contr, Considering fields where you have applied water, how much do those crops contribute to your overall income?F08_emply_lab, In the most recent cultivation season, did you employ day labourers on fields?F09_du_labour, In the most recent cultivation season, did anyone in the household undertake day labour work on other farm?F10_liv_owned, What types of livestock do you own? (list)F10_liv_owned_other, What types of livestock do you own? (not on list)F_liv_count, Livestock countF12_poultry, Own poultry?F13_du_look_aftr_cows, At the present time, does the household look after cows for someone else in return for milk or money?F14_items_owned, Which of the following items are owned by the household? (list)F14_items_owned_other, Which of the following items are owned by the household? (not on list)G01_no_meals, How many meals do people in your household normally eat in a day?G02_months_lack_food, Indicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?G03_no_food_mitigation, When you have faced such a situation what do you do?gps:Latitude, Location latitude (provided by smartphone)gps:Longitude, Location Longitude (provided by smartphone)gps:Altitude, Location Altitude (provided by smartphone)gps:Accuracy, Location accuracy (provided by smartphone)instanceID, Unique identifier for the form data submission

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL

Keep track of the location of the property in the address list.

Access resources are designed to help anyone who can find information on the location of people, property, structures, areas or other things needed. Access addresses are thus used directly by the public or through various organisations, such as government authorities, emergency services, investigative bodies, utility companies and others.

The term address is an access address. An address contains both descriptive and geometrical location information. Descriptive information, for example, refers to which municipality, town or neighborhood address is located and on which street it is located. Geometric information means two-dimensional coordinates of a harmonised national coordinate system.

The address list contains records for over 95 % of all registered real estate in Registers Iceland’s real estate register. Most addresses correspond to structures or structures. Where there is no structure on land, the land itself receives an address.

Basically, the address is the destination. It refers to the entrance of a construction or other type of entry. The relationship between addresses and land is such that many addresses can be linked to each land, but only one land is linked to each address. Thus, for example, each of the stairways of a multi-dwelling house can have its own address.

The number of dwellings by dwelling occupancy, shared dwellings, accommodation type, tenure, central heating type and number of bedrooms. Data are available at country, region, local authority, Middle layer Super Output Area and Lower layer Super Output Area in England and Wales, where possible.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES RESIDENCE 1 YEAR AGO - DP02 Universe - Population 1 year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 For the ACS, people who had moved from another residence in the United States or Puerto Rico 1 year earlier were asked to report the exact address (number and street name); the name of the city, town, or post office; the name of the U.S. county or municipio in Puerto Rico; state or Puerto Rico; and the ZIP Code where they lived 1 year ago. People living outside the United States and Puerto Rico were asked to report the name of the foreign country or U.S. Island Area where they were living 1 year ago.

These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/" class="govuk-link">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority" class="govuk-link">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 27 June 2025 and was updated with provisional data from completed transactions during May 2025.

The next release will be published 09:30 31 July 2025 and will be updated with provisional data from completed transactions during June 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above" class="govuk-link">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities. This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Residential Property Prices in India increased 3.10 percent in December of 2024 over the same month in the previous year. This dataset includes a chart with historical data for India Residential Property Prices.

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 and the group quarters population 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, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..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.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.