The "Utah 64 Small Health Statistics Areas" feature layer was developed by the Office of Public Health Assessment, Utah Department of Health using small area analysis methodology in 1997. Each feature was generated by combining a sufficient number of adjacent ZIP code area features to form a geographic area of approximately 33,500 persons (range 15,000 to 62,500). Criteria used for determining which ZIP code areas to combine together to form a Small Health Statistics Area included population size, local health district and county boundaries, similarity of ZIP code population's income level and community political boundaries. Input from local community representatives was used to refine area designations. The Utah 64 Small Health Statistics Areas provide a means of geographically analyzing and presenting health statistics at the community level. Producing information at the small area in Utah provides community planners and other with information that is specific to the populations living in their communities of concern. Small area analysis also allows an investigator to explore ecologic relationships between health status, lifestyle, the environment and the health system. In areas where a ZIP code crosses a county boundary, the 2008 and 2009 versions of Small Statistical Areas honor the ZIP code boundary leading to cases where a Small Statistical Areas can be in multiple counties. The 2012 and 2014 versions correct this issue by splitting ZIP code areas by county boundaries resulting in Small Statistical Areas that can only be found in one county. In the 2017 version, area 57 Grand/San Juan Counties was split into 2 areas, area 57.1 Grand county and 57.2 San Juan County.

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 Census 2001: Small Area Microdata for Imputation Analysis (SAM) is a 5% sample of individuals for all countries of the UK, with 2.96 million cases. Local Authority is the lowest level of geography for England and Wales, Council Areas for Scotland and Parliamentary Constituencies for Northern Ireland. The Scilly Isles have been merged with Penwith and the City of London with Westminster. Orkney and Shetland are merged into one area. All other areas are identified. The median sample size for an authority is 5,600 records and nearly eighty authorities have more than 10,000 records. The amount of individual detail in the SAM is less than in the 2001 Individual Licenced Sample of Anonymised Records (I-SAR)(see under SNs 7205 and 7206) because of the greater geographical detail in the SAM.

Caveat - Students:
As with the Individual SAR, the SAM includes those enumerated in a communal establishment and also full-time students who were enumerated at an address that was not their usual term-time residence. For the latter there is only individual-level information on age, sex, marital status and full-time student status. It is recommended that these students are not included in any analyses as they do not form part of the usual residents population base.
This dataset contains 155 variables, including 67 imputation flag variables. The standard version, containing 88 SAM variables, is available under SN 7207.


Main Topics:
Accommodation type (brief)Accommodation type (detailed)
Adults, Number Employed in Household
Adults, Number in Household
Age
Age of Family Reference Person (FRP)
Age of Household Reference Person (HRP)
Age of Students and Schoolchildren
Amenities
Armed Forces
Bath/Shower and Toilet, use of
Care (unpaid), Provision of
Care, Provision of
Carers and their Economic Activity, Number of
Cars and vans
Central heating
Children
Children, dependent
Communal Establishment Residents
Communal establishment, combined type and management
Concealed families
Country of birth
Country of Birth (additional categories)
Daytime Population
Dwelling Type
Economic Activity
Economic Activity of Associated People Resident in Households
Economic Activity of Full-time students
Economic Activity of Household Reference Person (HRP)
Ethnic group (England and Wales)
Ethnic group (England and Wales) of Household Reference Person
Family composition
Family status
Family type
Health, General
Hours worked
Household composition
Household composition (alternative classification)
Household dependent children
Household deprivation
Household Reference Person indicator
Household size
Household Space Type
Household Type
Households with students away during term-time
Industry
Industry, former
Limiting long-term illness
Limiting Long-Term Illness (LLTI), Household residents with
Limiting long-Term Illness, number of people with in household
Living arrangements
Living arrangements of Household Reference Person (HRP)
Lowest floor level
Marital status
Migration (armed forces)
Migration (Communal establishment)
Migration (People)
Multiple ethnic identifier
Occupancy Rating
Occupation (brief)
Occupation (detailed)
Occupation, former
Pensioner household
People aged 17 or over in household, Number of
Population Type
Public transport users in households
Qualifications (England and Wales)
Qualifications, highest level of (England and Wales)
Qualifications, professional
Religion (England and Wales)
Religion (England and Wales) of Household Reference Person
Resident Basis
Resident Type
Rooms in a dwelling, number of
Rooms, Number of
Rooms, Persons per
Sex
Sex of Household Reference Person (HRP)
Single Adult Households
Social Grade of Household Reference Person (HRP), approximated
Social Grade, approximated
Socio-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 of
Student accommodation (Standard Output)
Student accommodation Type
Student...

Introduction: The location of residence is a factor possibly contributing to social inequalities and emerging evidence indicates that it already affects perinatal development. The underlying pathways remain unknown; theory-based and hypothesis-driven analyses are lacking. To address these challenges, we aim to establish to what extent small-area characteristics contribute to low birth weight (LBW), independently of individual characteristics. First, we select small-area characteristics based on a conceptual model and measure them. Then, we empirically analyse the impact of these characteristics on LBW.Material and methods: Individual data were provided by the birth cohort study “Health of infants and children in Bielefeld/Germany.” The sample consists of 892 eligible women and their infants distributed over 80 statistical districts in Bielefeld. Small-area data were obtained from local noise maps, emission inventory, Google Street View and civil registries. A linear multilevel analysis with a two-level structure (individuals nested within statistical districts) was conducted.Results: The effects of the selected small-area characteristics on LBW are small to non-existent, no significant effects are detected. The differences in proportion of LBW based on marginal effects are small, ranging from zero to 1.1%. Newborns from less aesthetic and subjectively perceived unsafe neighbourhoods tend to have higher proportions of LBW.Discussion: We could not find evidence for negative effects of small-area factors on LBW, but our study confirms that obtaining adequate sample size, reliable measure of exposure and using available data for operationalisation of the small-area context represent the core challenges in this field of research.

Analysis of ‘Small Areas Generalised 20m - OSi National Statistical Boundaries - 2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/3370bbe0-278e-453d-a83b-2e023ae19420 on 16 January 2022.

--- Dataset description provided by original source is as follows ---

The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets in the ITM projection.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Small Areas Generalised 100m - OSi National Statistical Boundaries - 2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ead1416a-8151-439f-8034-c6cc926d0864 on 16 January 2022.

--- Dataset description provided by original source is as follows ---

The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets in the ITM projection.

--- Original source retains full ownership of the source dataset ---

This dataset consists of housing unit, household, and population estimates for census tracts, census block groups, Transportation Analysis Zones (TAZs), school districts, and ZIP codes in the Twin Cities Region. These data provide a more precise and timely picture of current conditions than the American Community Survey, another source of small area data that is better suited for statistics like percentages and averages than for actual counts. It may be possible to calculate estimates for other small areas upon request; contact Research@metc.state.mn.us for more information.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

This dataset is for examples in the Ecography Software Note, FIESTA: A Forest Inventory Estimation and Analysis R package, by Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul, L.; Toney, Chris; White, Grayson W. The examples demonstrate how to generate estimates of forest attributes using three different FIESTA modules: Green Book (GB), Model-Assisted (MA), and Small Area (SA). Included in the dataset are: a geospatial vector shapefile (.shp) of the Middle Bear-Logan Watershed area of interest (AOI); an R sf object (.rds) defining an ecological extent encompassing the AOI, Ecomap Section M331D (Cleland et al. 2007) ; a SQLite database (.db) including FIA plot data downloaded from FIA's publicly available DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html) and subset to the M331D boundary; and five auxiliary spatially-explicit raster layers (.img) clipped to the M331D boundary.

BackgroundThere has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.Methods and FindingsWe used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother's set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2–45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3–55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.ConclusionsThe new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival.Please see later in the article for the Editors' Summary

Analysis of ‘Loudoun Small Area Plans’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8ccf1088-d29b-492f-981a-afb01b6c8485 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This layer establishes the boundaries for Small Area Plans, authorized under Code of Virginia Section 15.2-2303.4, encompassing the Urban Policy Areas, Suburban Policy Area, Transition Policy Area, Leesburg JLMA, and the three Silver Line Metrorail Stations within the County. They areas are exempt from the proffer legislationprovisions established by Code of Virginia Section 15.2-2303.4.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘PLACES: Local Data for Better Health, County Data 2021 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/35b1223f-c3b3-42f9-9f0a-d5ed3e59ee85 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains model-based county-level estimates for the PLACES 2021 release. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 4 chronic disease-related health risk behaviors, 13 health outcomes, 3 health status, and 9 on using preventive services. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2019 or 2018 county population estimate data, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS because the relevant questions are only asked every other year in the BRFSS. More information about the methodology can be found at www.cdc.gov/places.

--- Original source retains full ownership of the source dataset ---

🇮🇪 아일랜드 English Small Areas were designed as the lowest level of geography for the dissemination of statistics and generally comprise either complete or part of townlands or neighbourhoods. Small Areas were created by The National Institute of Regional and Spatial Analysis (NIRSA) on behalf of the Tailte Éireann (TE) in consultation with CSO.Small Areas generally comprise between 80 and 120 dwellings and nest within CSO Electoral Divisions.The Small Area boundaries have been amended based on Census 2022 population data.Generalised data: provided for information only.Update Notice: 4th August 2023: Attribution changed for ED and LEA attributes. An implication of this is CSO ED increase in count from 3419 to 3420 and CSO LEA boundary changes. ED and LEAs impacted are

These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

SUMMARYTo be viewed in combination with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.This dataset shows where there was no data* relating to one of more of the following factors:Obesity/inactivity-related illnesses (recorded at the GP practice catchment area level*)Adult obesity (recorded at the GP practice catchment area level*)Inactivity in children (recorded at the district level)Excess weight in children (recorded at the Middle Layer Super Output Area level)* GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices.GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. This dataset identifies areas where data from 2019/20 was used, where one or more GPs did not submit data in either year (this could be because there are rural areas that aren’t officially covered by any GP practices), or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution.Results of the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ analysis in these areas should be interpreted with caution, particularly if the levels of obesity, inactivity and associated illnesses appear to be significantly lower than in their immediate surrounding areas.Really small areas with ‘missing’ data were deleted, where it was deemed that missing data will not have impacted the overall analysis (i.e. where GP data was missing from really small countryside areas where no people live).See also Health and wellbeing statistics (GP-level, England): Missing data and potential outliers dataDATA SOURCESThis dataset was produced using:- Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.- National Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. - Active Lives Survey 2019: Sport and Physical Activity Levels amongst children and young people in school years 1-11 (aged 5-16). © Sport England 2020.- Active Lives Survey 2019: Sport and Physical Activity Levels amongst adults aged 16+. © Sport England 2020.- GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.- Administrative boundaries: Boundary-LineTM: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.- MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Sport England 2020; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Small Areas are areas of population comprising between 50 and 200 dwellings created by The National Institute of Regional and Spatial Analysis(NIRSA) on behalf of the Ordnance Survey Ireland(OSi) in consultation with CSO. Small Areas were designed as the lowest level of geography for the compilation of statistics in line with data protection and generally comprise either complete or part of townlands or neighbourhoods. There is a constraint on Small Areas that they must nest within Electoral Division boundaries. The small area boundaries have been amended in line with population data from Census 2011

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Analysis of ‘All-Island General Health (SA)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/16f846ca-d301-440b-ba4a-7ac05ed88c9a on 18 January 2022.

--- Dataset description provided by original source is as follows ---

This file contains variables from the Health Theme - General Health that was produced by AIRO using data from the census unit at the CSO and the Northern Ireland Research and Statistics Agency (NISRA). This data was developed under the Evidence Based Planning theme of the Ireland Northern Cross Border Cooperation Observatory (INICCO-2) and CrosSPlaN-2 funded research programme. The file includes data on 23,025 Small Areas within the Republic of Ireland and Northern Ireland. For more information on the original data source please see http://www.cso.ie/en/index.html and http://www.nisra.gov.uk/

--- Original source retains full ownership of the source dataset ---

Spatial layer of small areas used for the City of Melbourne's Census Of Land Use And Employment (CLUE) analysis. Note that these small area boundaries do not exactly correspond with gazetted suburb or postcode boundaries.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

About this dataset

Context

The dataset tabulates the Little Valley population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Little Valley across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Little Valley was 1,059, a 1.03% decrease year-by-year from 2022. Previously, in 2022, Little Valley population was 1,070, a decline of 0.83% compared to a population of 1,079 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Little Valley decreased by 62. In this period, the peak population was 1,140 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Little Valley is shown in this column.
• Year on Year Change: This column displays the change in Little Valley population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Little Valley Population by Year. You can refer the same here

Mean of the RMSEs obtained in the estimation of the relative risks for the small areas and 95% Bayesian prediction interval for the difference between the RMSEBYM and the RMSEmod that would be obtained after applying the convolution model and the proposed model to a new data set.