Analysis of ‘Community Districts (Water Areas Included)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c41c97e9-eaf4-429d-9c86-b59ad8c2e873 on 13 February 2022.

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

GIS data: Community Districts (Water areas included)

Community Districts are mandated by the city charter to review and monitor quality of life issues for New York City (NYC) neighborhoods. NYC is currently comprised of 59 community districts. The first byte is a borough code and the second and third bytes are the community district number. There are also 12 Joint Interest Areas (JIAs). The JIAs are major parks and airports and are not contained within any community district. This dataset is being provided by the Department of City Planning (DCP) for informational purposes only. DCP does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of the dataset, nor are any such warranties to be implied or inferred with respect to the dataset as furnished on the website. DCP and the City are not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use the dataset, or applications utilizing the dataset, provided by any third party.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

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

Analysis of ‘Community Reporting Areas’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/73623efd-3088-49c7-b51b-cdbc119b2af8 on 12 February 2022.

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

Community reporting areas (CRAs) are designed to address a gap that existed in city geography. The task of reporting citywide information at a "community-like level" across all departments was either not undertaken or it was handled in inconsistent ways across departments. The CRA geography provides a "common language" for geographic description of the city for reporting purposes. Therefore, this geography may be used by departments for geographic reporting and tracking purposes, as appropriate. The following criteria for a CRA geography were defined for this effort: -no overlapping areas; -complete coverage of the city; -suitable scale to represent neighborhood areas/conditions; -reasonably stable over time; -consistent with census geography; -relatively easy to use in a data context; -familiar system of common place names; and, -respects neighborhood district geography. The following existing geographies were reviewed during this effort: -neighborhood planning areas (DON); -neighborhood districts (DON/CNC/Neighborhood District Councils); -city sectors/neighborhood plan implementation areas (DON); -urban centers/urban villages (DPD); -population sub-areas (DPD); -Neighborhood Map Atlas (City Clerk); -Census 2000 geography; -topography; and, -various other geographic information sources related to neighborhood areas and common place names. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.

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

The Access to Parks indicator measures the number of residential parcels in the Champaign-Urbana-Savoy urbanized area that are within one half mile and within one quarter mile of a public park or other public open space. The half mile and quarter mile are used here as representations of reasonable walking distance to an amenity, with a half mile generally representing a 10 minute walk, and a quarter mile representing a 5 minute walk.

Public parks, public golf courses, forest preserves, and public/private recreational facilities (i.e. privately owned recreational land available for public use) were counted toward this indicator. Private golf courses and country clubs were not counted toward this indicator.

The access analysis was performed using linear distance, rather than distance along a street network, so access in some areas may be limited by characteristics of the street network (e.g., form, lack or condition of sidewalks) or major barriers (e.g., highways and other wide roads that are difficult or dangerous to cross).

Taking into account the limitations of our methodology, as of the analysis performed in June 2022, Champaign-Urbana-Savoy residents as a whole have very good access to parks and open space: over 74 percent of the Champaign-Urbana-Savoy residential area is within one quarter mile of a park or open space, and almost 97 percent of the Champaign-Urbana-Savoy residential area is within one half mile of a park or open space.

Parks and open space are valuable amenities that have recreational, environmental, and public health benefits. The ability of residents to visit parks and access these benefits without a car is a measure of both equity and quality of life.

The percentage analysis was performed in GIS using map layers from the Champaign County Regional Planning Commission (CCRPC) and Champaign County GIS Consortium (CCGISC). The analysis is done on an annual basis, to account for any changes in both parks and residential areas.

Historical information about the total Employees and Businesses Dataset is a snapshot of the total number of businesses that are currently in Mesa, as well as the total number of employees that work in Mesa. Source: ESRI Community Analyst. It is important to note that in this dataset, a “Full-Time Employee (FTE)” in Mesa is someone who may not necessarily live in Mesa, however, they are employed at a business that is located in Mesa. This is a distinct difference between the “Employment” number in Mesa, which is stated in the “Employment Dataset.” Employment refers to the total number of Mesa residents that are employed, within or outside of the City of Mesa.

This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioural attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

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).

Los Angeles Index of Displacement PressureThe Los Angeles Index of Displacement Pressure combines measures that past research efforts and our own original research have shown correlate with future change and displacement pressure. Created in 2015/2016, the index primarily uses data from 2012-2015.These seven measures are applied at the Census Tract level for tracts where >=40% of households earn less than the City's median income. The measures are grouped into two classes: change factors and displacement pressure factors.Change factor measures are those that suggest future revitalization is likely due to investment, projected housing price gains, and proximity to recently changed areas. On the other hand, displacement pressure factors capture areas with a high concentration of existing residents who may have difficulty absorbing massive rent increases that often accompany revitalization. The Los Angeles Index of Displacement Pressure captures the intersection between these two classes.Change Measures Transportation InvestmentMeasure 1: Distance to current rail stations (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure). Source: LA MetroMeasure 2: Distance to rail stations under construction/recently opened in 2016 (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure)Source: LA Metro Proximity to Rapidly Changing NeighborhoodsMeasure 3: Distance to the closest "top tier" changing neighborhood, as defined by the Los Angeles Index of Neighborhood Change (within a 1 mile radius. Tracts beyond 1 mile receive no score for this measure)Source: The Los Angeles Index of Neighborhood Change Housing MarketMeasure 4: Change in housing price projections from 2015 to 2020 Source: ESRI Community Analyst Displacement Pressure FactorsMeasure 5: Percent of households that rentSource: American Community Survey, Five-Year Estimate, 2014Measure 6: Percent of households that are extremely rent burdened (pay >=50% of household income on rent)Source: American Community Survey, Five-Year Estimate, 2014Measure 7: The number of affordable properties and housing units that are due to expire by 2023.Source: The Los Angeles Housing Element, 2012Date updated: April 7, 2018Refresh rate: Never - Historical data

Analysis of ‘Community Board Leadership’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3a3851e3-c1f8-4eaa-a1da-84b41988417b on 27 January 2022.

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

This is a comprehensive list of each Community Board and its respective Chair and District Manager. The list also includes the address, meeting time and telephone number of each Community Board. Constitutents may use the list as an informational tool to contact their Community Boards and be able to attend meetings.

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

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 51.85% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Springfield is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Springfield total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Springfield Population by Race & Ethnicity. You can refer the same here

Collaboration networks are defined as a set of individuals who come together and collaborate on particular tasks such as publishing a paper. The analysis of such networks permits to extract knowledge on the structure and patterns of communities. The link definition and network extraction have a high impact on the analysis of collaboration networks. Previous studies model the connectivity in a network considering it as a binomial problem with respect to the existence of a collaboration between individuals. However, such a data consists of a high diversity of features that describe the quality of the interaction such as the contribution amount of each individual. In this paper, we have determined a solution to extract collaboration networks using corresponding features in a dataset. We define collaboration score to quantify the collaboration between collaborators. In order to validate our proposed method, we benefit from a scientific research institute dataset in which researchers are co–authors who are involved in the production of papers, prototypes, and intellectual properties (IP). We evaluated the generated networks, produced through different thresholds of collaboration score, by employing a set of network analysis metrics such as clustering coefficient, network density, and centrality measures. We investigated more the obtained networks using a community detection algorithm to further discuss the impact of our model on community detection. The outcome shows that the quality of resulted communities on the extracted collaboration networks can differ significantly based on the choice of the linkage threshold.

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

For each methodology a “clustering control” was run to determine how many OTUs would be expected in the absence of errant reads.*Missing OTU numbers exclude reference sequences that were unidentifiable in the raw dataset (5 missing in V3V4P dataset and 1 missing in V6P dataset).

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Analysis of ‘Micro Community Policing Plans’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9f4fb539-0715-44fe-a26e-7848e39f2de0 on 27 January 2022.

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

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

Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation.However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of approximately 5,000 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition t..., , The database has been split into seven data parts and the empty Bern_ECochG database to facilitate downloading. Each part is saved as a .csv file and can be imported into the Bern_ECochG database individually. We recommend downloading all parts and assembling them using sqlitebrowser, available at https://sqlitebrowser.org/. The Python scripts provided will only work when the database is fully assembled. The Python scripts show how to access the database. Along with the Python scripts, a .yml file is provided to install all dependencies to run the scripts.

About this dataset

Context

The dataset tabulates the population of Sheridan by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Sheridan. The dataset can be utilized to understand the population distribution of Sheridan by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Sheridan. 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 Sheridan.

Key observations

Largest age group (population): Male # 15-19 years (184) | Female # 50-54 years (153). 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 Sheridan population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sheridan is shown in the following column.
• Population (Female): The female population in the Sheridan 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 Sheridan 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 Sheridan Population by Gender. You can refer the same here

Analysis of ‘United States COVID-19 County Level of Community Transmission as Originally Posted’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/349a4a03-e00c-452c-82d8-3fb1e19855cb on 11 February 2022.

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

This public use dataset has 7 data elements reflecting community transmission levels for all available counties. This dataset contains reported daily transmission level at the county level and contains the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Currently, CDC provides the public with two versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Originally Posted dataset), updated daily with the most recent day’s data, and an historical dataset with the county-level transmission data from January 1, 2021 (Historical Changes dataset).

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have a transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected.

Transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank : total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data tha

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

Analysis of ‘Roscommon Community Parks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/77e567d9-f336-45b8-bcd0-b87f7b76ce98 on 13 January 2022.

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

Public and Community Parks in County Roscommon.

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

Analysis of ‘Employed population by frequency with which they work Saturdays, sex and Autonomous Community. Percentages with respect to the total of each Autonomous Community. EPA (API identifier: 37011)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-347-37011 on 07 January 2022.

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

Table of INEBase Employed population by frequency with which they work Saturdays, sex and Autonomous Community. Percentages with respect to the total of each Autonomous Community. Annual. Autonomous Communities and Cities. Economically Active Population Survey

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