The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

DUIA includes data on the socio-economic development and amenities of 86 cities from a total of 32 countries. DUIA is based on freely and easily available data sources and built on integration protocols and codes in R scripts, making both the construction of the database as a whole and specific statistical analyses fully transparent and replicable. DUIA is constructed in three steps. First, we draw upon remote sensing derived data from the Atlas of Urban Expansion to define city boundaries as accurately and consistently as possible across the different countries. Second, we draw upon survey data stored in IPUMS (Integrated Public Use Microdata Series) to include extensive, harmonized, and disaggregated data. Third, as we especially seek to contribute to comparative research outside the West, we developed tailor-made solutions to include Indian and Chinese cities for which data were not available in IPUMS.

Abstract of associated article: We explore the effect of cross-sectional aggregation of data on estimation and test of asymmetric retail fuel price responses to wholesale price shocks. The analysis is performed on data collected daily from individual fuel stations in the Spanish metropolitan areas of Madrid and Barcelona. While the standard OLS estimator is applied to an error correction model in the case of the aggregated time series, we use the mean group approaches developed by Pesaran and Smith (1995) and Pesaran (2006) to estimate the short- and long-run micro-relations under heterogeneity. We found remarkable differences between the results of estimations using aggregated and disaggregated data, which are highly robust to both datasets considered. Our findings could help to explain many of the results in the literature on this research topic. On the one hand, they suggest that the typical estimation with aggregated data clearly tends to overestimate the persistence of shocks. On the other hand, we show that aggregation may generate a loss of efficiency in econometric estimates that is sufficiently large to hide the existence of the “rockets and feathers” phenomenon.

The new scorecard tracks progress toward the World Bank Group's vision to create a world free of poverty on a livable planet. The Scorecard includes three types of indicators: - Vision indicators - reflect the new vision for the WBG, showing the WBG’s ambition and providing high-level measures to gauge the direction and pace of progress in tackling global challenges. Vision indicators contain aggregated and disaggregated development context data for all countries in the world, where data is available. The Scorecard reports the latest available global updates for each of these indicators. - Client context indicators - reflect the circumstances in client countries, including multidimensional aspects of poverty, and are aligned with the Sustainable Development Goals (SDGs). They serve to frame the challenges clients face, and the context in which the WBG operates. Client Context indicators contain aggregated and disaggregated development context data for World Bank client countries, based on country eligibility for financing and where data is available. The Scorecard also reports the latest available update for each of these indicators. - WBG Results indicators monitor WBG progress on some of the most critical global challenges. Results data include: - Active Portfolio Results: Contain achieved and expected results of WBG operations based on its active portfolio as of end of June 2024. Includes aggregated and disaggregated data. - Results achieved since July 1st, 2023: Contain cumulative results achieved between July 1st, 2023 - June 30, 2024 from active and closed projects. Results achieved before July 1st, 2023 are excluded from this calculation. Includes aggregated data for World Bank, IBRD and IDA only. IFC and MIGA do not currently report this data. - Operations Details: Operation-level detail is provided for World Bank projects. However, in alignment with IFC and MIGA Access to Information Policies, project-level data is available in an aggregated format on the WBG Scorecard, provided the minimum threshold to secure individual clients' data is satisfied. This collection includes only a subset of indicators from the source dataset.

Currently published data series on the United States household debt service ratio are constructed from aggregate household debt data provided by lenders and estimates of the average interest rate and loan terms of a range of credit products. The approach used to calculate those debt service ratios could be prone to missing changes in loan terms. Better measurement of this important indicator of financial health can help policymakers anticipate and react to crises in household finance. We develop and estimate debt service ratio measures based on individual-level debt payments data obtained from credit bureau data and published estimates of disposable personal income. Our results suggest that aggregate debt service ratios may have understated the payment requirements of households. To the extent possible with two very distinct data sources we examine the details on the composition of household debt service and identify some areas where required payments appear to have varied substantially from the assumptions used in the Board of Governors' aggregate calculation. We then use our technique to calculate both national and state-level debt ratios and break these debt service ratios into debt categories at the national, state level, and metro level. This approach should allow detailed forecasts of debt service ratios based on anticipated changes to interest rates and incomes, which could serve to evaluate the ability of households to cope with potential economic shocks. The ability to disaggregate these estimates into geographic regions or age groups could help to identify the severity of the effects on more exposed groups.

This dataset is one of the outputs of the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) for 2010, which includes physical area, harvest area, production and yield, for 42 crops, disaggregated at the input-levels (e.g., irrigated/rainfed and high/low-input) on a 10 km grid globally. Production values in this dataset are given for each technology aggregated by categories - crops/food/non-food - with no information on individual crops. Unit of measure: Production for each technology: mt This new version of MapSPAM, available to download from the Harvard Dataverse Website, marks the third generation of the SPAM data series, following those of 2000 and 2005. More information on the production systems and selected crops is available in the Global Spatially-Disaggregated Crop Production Statistics Data (MapSPAM) full metadata at https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

Пилотная база данных по агрегированной государственной занятости на субнациональном уровне (Aggregated SUBEMP) содержит подборку показателей государственной занятости с гендерной точки зрения для субнациональных органов управления в странах ОЭСР и ЕС. Он дополняется дезагрегированными данными, то есть данными по отдельным субнациональным органам власти в данной стране, которые можно загрузить непосредственно по ссылкам, приведенным ниже.Данные были собраны за последний доступный год с охватом времени с 2019 по 2023 год (в зависимости от наличия данных).. Данная база данных подготовлена в рамках ОЭСР/ЕС совместный проект усиление аналитических структур и данных на субнациональном государственных финансов и занятости населения и, с целью сбора, стандартизации и распространения высококачественных сопоставимых данных для органов местного самоуправления финансов и занятости. Эти данные предоставляют информацию для оценки потенциала и полномочий муниципалитетов и регионов по принятию решений, которые играют важную роль в разработке политики, ориентированной на конкретные места. Это также позволяет проводить сравнения внутри страны и между странами, что проливает свет на различия между субнациональными правительствами с точки зрения выполнения ими своих мандатов и их способности получать доходы из собственных источников. Более подробную информацию об обязанностях субнациональных правительств вы найдете в Всемирной обсерватории по финансам и инвестициям субнациональных правительств. The pilot database on aggregated subnational public employment (Aggregated SUBEMP) provides a selection of indicators on public employment with a gender perspective for subnational administrations in OECD and EU countries. It is complemented by disaggregated data, that is data for individual subnational governments in a given country, which can be downloaded directly from the links below.Data was collected for the latest year available with the time coverage ranging from 2019 to 2023 (based on data availability). This database has been produced in the context of the OECD/EU joint project Strengthening analytical frameworks and data on subnational government finance and public employment, with the goal of collecting, standardising, and disseminating high-quality comparable data for local government finance and employment. This data provides information to assess the capacities and decision-making power of municipalities and regions, whose role is essential in developing place-based policy. It also allows for within and cross-country comparisons, which sheds light on the disparities between subnational governments in terms of fulfilling their mandates and their ability to raise own-source revenue. You will find more information on subnational government responsibilities in the World Observatory on Subnational Government Finance and Investment.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. If you have questions about the underlying data stored here, please contact the Environmental Protection Agency using their RSEI Contact Form https://www.epa.gov/rsei/forms/contact-us-about-rsei-model or email Mitchell Sumner (sumner.mitchell@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu The Geographic Microdata from EPA's Risk-Screening Environmental Indicators (RSEI) model are unique datasets that provide detailed air and water modeling results at various levels of aggregation, spatial geographies, and time periods for data user needs. RSEI Geographic Microdata allow for a flexible ability to compare and analyze RSEI model outputs and results from a receptor-based perspective of potentially impacted geographic areas. These data include values related to each modeled chemical release to air and water and each potentially impacted geographic area located around the facilities that ultimately release the chemical into the environment. Users can examine the potential impacts that environmental releases of toxic chemicals from multiple facilities may have on a particular area, regardless of where the releases originate, and get a more realistic picture of the degree to which the area is potentially affected by TRI chemical releases. Underlying these results is the ability to locate facilities, environmental releases, and people geographically and attribute characteristics of the physical environment such as meteorology, hydrography, and topography on surrounding areas once they are located to estimate potential exposure and relative health impacts. The RSEI model describes the U.S. and its territories using a grid-based system and a surface water network. Facility- and chemical-specific data retrieved from Agency-reported informational data sources (such as site addresses and lat/long coordinates) are then geographically indexed to their corresponding grid cell in the grid system or stream segment (flowline) in the surface water network for modeling purposes. The RSEI air modeling and RSEI water modeling pages contain more information on how RSEI models these types of releases. [Quote from: https://www.epa.gov/rsei/rsei-geographic-microdata-rsei-gm] Data for United States Environmental Protection Agency (EPA) Risk Screening Environmental Indicators (RSEI) model Disaggregated Microdata, 2017, 1988-2017 data. Original data were downloaded 2 August 2025 from http://abt-rsei.s3-website-us-east-1.amazonaws.com/?prefix=microdata2017/. Documentation pdf was downloaded from here: https://www.epa.gov/sites/default/files/2017-01/documents/rsei-documentation-geographic-microdata-v235.pdf . RSEI data are distributed in aggregated and disaggregated forms. Disaggregated data has separate results, concentrations, and toxicity-weighted concentrations for each modeled chemical release for each unit of analysis (810m grid cell, block group census tract, etc.). Aggregated data includes scores summed for all chemical releases. The data in this deposit is disaggregated and distributed at the 810m grid cell level. For RSEI Aggregated Microdata 2017, see: https://sciop.net/uploads/bf258852c37fa1cae8512c49cda65b2a83403c9f, and for other components of the Disaggregated 2017 Microdata, see Zenodo repositories: https://zenodo.org/records/17065165 (Census Agg); https://doi.org/10.5281/zenodo.17065220 (Census Full pt 1); https://zenodo.org/records/17088034 (Census Full pt 2); https://zenodo.org/records/17109745 (Census Full pt 3); https://zenodo.org/records/17102039 (Census Full pt 4); https://zenodo.org/records/17109396 (Census Full pt 5); https://zenodo.org/records/17109593 (Shapefiles pt 1); https://zenodo.org/records/17127799 (Shapefiles pt 2) RSEI Disagg. Microdata 2017 (9) 1988-2017 data

Question 1.4.8c: Does the SOE or government publicly disclose the date of the production sales executed by the SOE? , 1.4.6b: Does the SOE publicly disclose its aggregate sales volume?, 1.4.6a: Does the SOE publicly disclose its aggregate production volume?, 1.2.1a: Does the government publicly disclose data on the volume of extractive resource production?, 1.2.1c: Is the data disclosed on the volume of extractive resource production machine-readable?, 1.2.2c: Is the data disclosed on the value of extractive resource exports machine-readable?, 1.2.2b: How up-to-date is the publicly disclosed data on the value of extractive resource exports?

Сводная база данных по финансам муниципального управления (Aggregated MUNIFI) содержит подборку показателей расходов, доходов и задолженности для всего сектора муниципального управления в странах ОЭСР и ЕС. Он дополнен дезагрегированными данными, то есть данными по каждому муниципалитету в данной стране, которые можно загрузить непосредственно по ссылкам, приведенным ниже. Временной охват охватывает период с 2010 по 2022 год (в зависимости от наличия данных). Данная база данных подготовлена в рамках ОЭСР/ЕС совместный проект усиление аналитических структур и данных на субнациональном государственных финансов и занятости населения и с целью сбора, стандартизации и распространения высококачественных сопоставимых данных для органов местного самоуправления финансов и занятости. Эти данные предоставляют информацию для оценки потенциала и полномочий муниципалитетов и регионов по принятию решений, которые играют важную роль в разработке политики, ориентированной на конкретные места. Это также позволяет проводить сравнения внутри страны и между странами, что проливает свет на различия между субнациональными правительствами с точки зрения выполнения ими своих мандатов и их способности получать доходы из собственных источников. Более подробную информацию об обязанностях субнациональных правительств вы найдете в Всемирной обсерватории по финансам и инвестициям субнациональных правительств. The aggregated municipal government finance database (Aggregated MUNIFI) provides a selection of indicators on expenditure, revenue, and debt for the entire municipal government sector in OECD and EU countries. It is complemented by disaggregated data, that is data for each municipality in a given country, which can be downloaded directly from the links below. The time coverage ranges from 2010 to 2022 (based on data availability). This database has been produced in the context of the OECD/EU joint project Strengthening analytical frameworks and data on subnational government finance and public employment with the goal of collecting, standardising, and disseminating high-quality comparable data for local government finance and employment. This data provides information to assess the capacities and decision-making power of municipalities and regions, whose role is essential in developing place-based policy. It also allows for within and cross-country comparisons, which sheds light on the disparities between subnational governments in terms of fulfilling their mandates and their ability to raise own-source revenue. You will find more information on subnational government responsibilities in the World Observatory on Subnational Government Finance and Investment.

Abstract: The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

This data comes from the annual MFRS (financial report) each institution files with the NCAA in January. Data goes back to FY2017. Some years are missing, as well as institutions (though institutions included are from both Division I and Division II). This file will be updated periodically as new financial reports are received.

Note that there are 2 separate files - Items Aggregated and Items Disaggregated.

Please enjoy using/analyzing the data but please attribute if you create/share any analysis.

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview: January 2025 municipal wards were collected in January 2025 by LTSB through LTSB's GeoData Collector. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2025 wards were estimated by aggregating 2020 US Census PL94-171 population data. LTSB has NOT topologically integrated the data. Election Data Overview: The 2024 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after the general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2020 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2025) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2020: Wards 2020 (Census 2020 totals used for disaggregation)Elections 2022: Wards 2022 (Census 2020 totals used for disaggregation)Elections 2024: Wards 2025 (Census 2020 totals used for disaggregation)Blocks -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined above.In the event that a ward exists now in which no block exists due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks This yields a block centroid layer that contains all elections from 1990 to 2024.Blocks [with all election data] -> Wards 2025 (then MCD 2025, and County 2025) All election data (including later elections) is aggregated to the Wards 2025 assignment of the blocks.Notes:Population of municipal wards 1991, 2001, 2011, 2020, 2022, and 2025 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2025.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2025. This is due to boundary corrections in the data from 2011 to 2025, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

These data show the main gross labour market series by region and department: number of jobseekers at the end of the month, entries on the job centre lists, exits from the job centre lists, offers collected and satisfied. The data are raw and rounded to 100; there may therefore be slight discrepancies between the sum of disaggregated data and the aggregated series. The ages chosen for the different series are the end-of-month ages (age that the jobseeker will have at the end of the month).

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data AttributesWard Data Overview: These municipal wards were created by grouping Census 2010 population collection blocks into municipal wards. This project started with the release of Census 2010 geography and population totals to all 72 Wisconsin counties on March 21, 2011, and were made available via the Legislative Technology Services Bureau (LTSB) GIS website and the WISE-LR web application. The 180 day statutory timeline for local redistricting ended on September 19, 2011. Wisconsin Legislative and Congressional redistricting plans were enacted in 2011 by Wisconsin Act 43 and Act 44. These new districts were created using Census 2010 block geography. Some municipal wards, created before the passing of Act 43 and 44, were required to be split between assembly, senate and congressional district boundaries. 2011 Wisconsin Act 39 allowed communities to divide wards, along census block boundaries, if they were divided by newly enacted boundaries. A number of wards created under Wisconsin Act 39 were named using alpha-numeric labels. An example would be where ward 1 divided by an assembly district would become ward 1A and ward 1B, and in other municipalities the next sequential ward number was used: ward 1 and ward 2. The process of dividing wards under Act 39 ended on April 10, 2012. On April 11, 2012, the United States Eastern District Federal Court ordered Assembly Districts 8 and 9 (both in the City of Milwaukee) be changed to follow the court’s description. On September 19, 2012, LTSB divided the few remaining municipal wards that were split by a 2011 Wisconsin Act 43 or 44 district line.Election Data Overview: Election data that is included in this file was collected by LTSB from the Government Accountability Board (GAB)/Wisconsin Elections Commission (WEC) after each general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. The ward data that is collected after each decennial census is made up of collections of whole and split census blocks. (Note: Split census blocks occur during local redistricting when municipalities include recently annexed property in their ward submissions to the legislature).Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the GAB/WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the GAB at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2010 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2011) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election Results: The process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from GAB/WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards spring 2017 (Census 2010 totals used for disaggregation)Blocks 2011 -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined aboveIn the event that a ward exists now in which no block exists (Occurred with spring 2017) due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks 2011This yields a block centroid layer that contains all elections from 1990 to 2016Blocks 2011 [with all election data] -> Wards 2011 (then MCD 2011, and County 2011) All election data (including later elections such as 2016) is aggregated to the Wards 2011 assignment of the blocksNotes:Population of municipal wards 1991, 2001 and 2011 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though municipal and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same. Therefore, data totals within a county should be the same between 2011 wards and 2018 wards.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2018. This is due to (a) boundary corrections in the data from 2011 to 2018, and (b) annexations, where a block may have been reassigned.

Users can view cross-nationally comparable data on the health workforce in the 193 WHO member states. Background The Global Atlas of the Health Workforce is a database maintained by the World Health Organization (WHO). This database allows users to view cross-nationally comparable data on the health workforce in the 193 WHO member states. Health workforce statistics includes the number or density of physicians, nurses, midwives, dentists, pharmacists, laboratory workers, community health workers, and public health workers. User Functionality Users can generate sta tistics pertaining to the health workforce. Users can view information by country, international region, or world, and choose a time period for which they are interested in viewing health workforce statistics. Aggregated and disaggregated data are available. In addition, users can view regional summaries of the health workforce. Data Notes The Global Atlas of the Health Workforce is updated periodically. Data are available for 1995-2011. Data are derived from national population censuses, labor force and employment surveys, health facility assessments, and official country reports to the WHO. Regional and country summaries are available.

This dataset contains data included in the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt.

Geographic data used to create maps in the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Geography/p6uw-qxpv

Filter by the Indicator column to select data for a particular indicator.

User notes: 10/9/25 - for the report year 2025, data for the following indicators were uploaded with changes relative to report year 2023: Crime Rate: As of January 1, 2021, the FBI replaced the Summary Reporting System (SRS) with the National Incident Based Reporting System (NIBRS), which expands how crimes were recorded and classified. This report uses California’s version of NIBRS, the California Incident Based Reporting System (CIBRS), obtained from the SANDAG Open Data Portal. Crime rates are not disaggregated by jurisdiction, as in the previous Equity Indicator Report. Internet access: The age group variable was incorporated to account for notable disparities in internet access by age. Police Stops and Searches: refined methods. Agency data was aggregated to San Diego County because data was available for all agencies; previously data was available for three agencies. Analysis of RIPA data was updated to exclude stops where the stop was made in response to a call for service, combine transgender women and transgender men into a transgender category, and limit to contraband found during search. Used term “discovery rate” instead of “hit rate.” Removed comparison to traffic collision data and instead compared to population estimates from the American Community Survey. Jail Incarceration: new data sources. The numerator data for the average daily population data in jail was obtained from the San Diego County Sheriff's Office. Population data to calculate the rates was obtained from the San Diego Association of Governments (SANDAG). The terms for conviction status were corrected to "locally sentenced" and "unsentenced" for sentencing status. For jail population data, East African was reclassified as Black and Middle Eastern as White to allow for calculation of rates using SANDAG population estimates.

8/1/25 - for the report year 2025, the following change were made: Business Ownership: the minority and nonminority labels were switched for the population estimates and some of the race/ethnicity data for nonemployer businesses were corrected. Homelessness: added asterisks to category name for unincorporated regions to allow for a footnote in the figure in the story page.

7/11/25 - for the report year 2025, the following changes were made: Beach Water Quality: the number of days with advisories was corrected for Imperial Beach municipal beach, San Diego Bay, and Ocean Beach.

5/22/25 - for the report year 2023, the following changes were made: Youth poverty/Poverty: IPUMS identified an error in the POVERTY variable for multi-year ACS samples. In July 2024, they released a revised version of all multi-year ACS samples to IPUMS USA, which included corrected POVERTY values. The corrected POVERTY values were downloaded, and the analysis was rerun for this indicator using the 2021 ACS 5-year Estimates. Youth Poverty: data source label corrected to be 2021 for all years. Employment, Homeownership, and Cost-Burdened Households - Notes were made consistent for rows where category = Race/Ethnicity.

5/9/25 - Excluding data for the crime section indicators, data were appended on May 9, 2025 and the report will be updated to reflect the new data in August 2025. The following changes in methods were made: For indicators based on American Community Survey (ACS) data, the foreign-born category name was changed to Nativity Status. Internet access: Group quarters is a category included in the survey sample, but it is not part of the universe for the analysis. For the 2025 Equity Report year, respondents in group quarters were excluded from the analysis, whereas for the 2023 Equity Report year, these respondents were included. Adverse childhood experiences - new data source.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview: July 2022 municipal wards were collected in July 2022 by LTSB through the WISE-Decade system. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2022 wards were estimated by aggregating 2020 US Census PL94-171 population data. LTSB has NOT topologically integrated the data. Election Data Overview: The 2022 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after the general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2020 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2022) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2020: Wards 2020 (Census 2020 totals used for disaggregation)Elections 2022: Wards 2022 (Census 2020 totals used for disaggregation)Blocks -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined above.In the event that a ward exists now in which no block exists due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks This yields a block centroid layer that contains all elections from 1990 to 2022.Blocks [with all election data] -> Wards 2022 (then MCD 2022, and County 2022) All election data (including later elections) is aggregated to the Wards 2022 assignment of the blocks.Notes:Population of municipal wards 1991, 2001, 2011, 2020 and 2022 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2022.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2022. This is due to boundary corrections in the data from 2011 to 2022, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

The erythrocyte sedimentation rate (ESR) test has been used for over a century. The Westergren method is routinely used in a variety of clinics. However, the mechanism of erythrocyte sedimentation remains unclear, and the 60 min required for the test seems excessive. We investigated the effects of cell aggregation during blood sedimentation and electrical conductivity at different hematocrits. A sample of blood was drop cast into a small chamber with two planar electrodes placed on the bottom. The measured blood conductivity increased slightly during the first minute and decreased thereafter. We explored various methods of enhancing or retarding the erythrocyte aggregation. Using experimental measurements and theoretical calculations, we show that the initial increase in blood conductivity was indeed caused by aggregation, while the subsequent decrease in conductivity resulted from the deposition of erythrocytes. We present a method for calculating blood conductivity based on effective medium theory. Erythrocytes are modeled as conducting spheroids surrounded by a thin insulating membrane. A digital camera was used to investigate the erythrocyte sedimentation behavior and the distribution of the cell volume fraction in a capillary tube. Experimental observations and theoretical estimations of the settling velocity are provided. We experimentally demonstrate that the disaggregated cells settle much slower than the aggregated cells. We show that our method of measuring the electrical conductivity credibly reflected the ESR. The method was very sensitive to the initial stage of aggregation and sedimentation, while the sedimentation curve for the Westergren ESR test has a very mild slope in the initial time. We tested our method for rapid estimation of the Westergren ESR. We show a correlation between our method of measuring changes in blood conductivity and standard Westergren ESR method. In the future, our method could be examined as a potential means of accelerating ESR tests in clinical practice.