About this dataset

Context

The dataset presents median household incomes for various household sizes in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, South Range did not include 4, 5, 6, or 7-person households. Across the different household sizes in South Range the mean income is $51,844, and the standard deviation is $18,238. The coefficient of variation (CV) is 35.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,226. It then further increased to $65,869 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/south-range-mi-median-household-income-by-household-size.jpeg" alt="South Range, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 South Range median household income. You can refer the same here

The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.

The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.

If you share or use this dataset, please cite [4] and [5] in any relevant documentation.

In addition, an image dataset for crack classification has also been published at [6].

References:

[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873

[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605

[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434

[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678

5 Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044

[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

Here's a sample Production Bates Range for a Gap Analysis exercise via Python. It's a CSV with one column containing a range of numbers following the convention "D0000001, D0000002, .... D0099999."

This script can be run against a variable/column on a document production index to identify document sequence gaps, which can be helpful to determine missing documents in a set or to diagnose a technical issue during data processing or exchange phases.

More broadly, this code can be updated to apply over any sequential data range (dates, student ID, serial number, item number, etc.), to show any gaps or available digits.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 52.64% of total population being male. 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.

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 South Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of South Range 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 South Range Population by Race & Ethnicity. You can refer the same here

In support of new permitting workflows associated with anticipated WellSTAR needs, the CalGEM GIS unit extended the existing BLM PLSS Township & Range grid to cover offshore areas with the 3-mile limit of California jurisdiction. The PLSS grid as currently used by CalGEM is a composite of a BLM download (the majority of the data), additions by the DPR, and polygons created by CalGEM to fill in missing areas (the Ranchos, and Offshore areas within the 3-mile limit of California jurisdiction).CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).Update Frequency: As Needed

This dataset tracks annual total students amount from 2013 to 2023 for Range View Elementary School

About this dataset

Context

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

Key observations

There is a considerable majority of female population, with 71.13% of total population being female. 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.

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 Grass Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Grass Range 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 Grass Range Population by Race & Ethnicity. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Palm Springs, CA.

About this dataset

Context

The dataset tabulates the South Range population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of South Range. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 373 (63.54% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the South Range population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in South Range is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the South Range is shown in the following column.

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 South Range Population by Age. You can refer the same here

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Bailey, CO.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

Project Goals: To identify regions of recently evolved endemic (neo-endemism) mammal species in California and thereby infer areas of rapid evolutionary diversification, which may help guide conservation prioritization and future planning for protected areas. Four species-based GIS rasters were produced of mammalian endemism (see reference for details ). This is: Richness of species distribution models weighted by inverse range size Abstract: The high rate of anthropogenic impact on natural systems mandates protection of the evolutionary processes that generate and sustain biological diversity. Environmental drivers of diversification include spatial heterogeneity of abiotic and biotic agents of divergent selection, features that suppress gene flow, and climatic or geological processes that open new niche space. To explore how well such proxies perform as surrogates for conservation planning, we need first to map areas with rapid diversification — ‘evolutionary hotspots’. Here we combine estimates of range size and divergence time to map spatial patterns of neo-endemism for mammals of California, a global biodiversity hotspot. Neo-endemism is explored at two scales: (i) endemic species, weighted by the inverse of range size and mtDNA sequence divergence from sisters; and (ii) as a surrogate for spatial patterns of phenotypic divergence, endemic subspecies, again using inverse-weighting of range size. The species-level analysis revealed foci of narrowly endemic, young taxa in the central Sierra Nevada, northern and central coast, and Tehachapi and Peninsular Ranges. The subspecies endemism-richness analysis supported the last four areas as hotspots for diversification, but also highlighted additional coastal areas (Monterey to north of San Francisco Bay) and the Inyo Valley to the east. We suggest these hotspots reflect the major processes shaping mammal neo-endemism: steep environmental gradients, biotic admixture areas, and areas with recent geological/climate change. Anthropogenic changes to both environment and land use will have direct impacts on regions of rapid divergence. However, despite widespread changes to land cover in California, the majority of the hotspots identified here occur in areas with relatively intact ecological landscapes. The geographical scope of conserving evolutionary process is beyond the scale of any single agency or nongovernmental organization. Choosing which land to closely protect and/or purchase will always require close coordination between agencies. Citation:DAVIS, E.B., KOO, M.S., CONROY, C., PATTON, J.L. & MORITZ, C. (2008) The California Hotspots Project: identifying regions of rapid diversification of mammals. Molecular Ecology 17, 120 -138. This dataset was reviewed in another manner. Spatial Resolution: 0.0083333338 DD This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

This dataset provides information about the number of properties, residents, and average property values for Range View Circle cross streets in Anderson, SC.

This dataset delineates the spatial range of wild pheasant populations in Minnesota as of 2002 by dividing the MN state boundary into 2 units: pheasant range and non-range.

We assess model performance using six datasets encompassing a broad taxonomic range. The number of species per dataset ranges from 28 to 239 (mean=118, median=94), and range shifts were observed over periods ranging from 20 to 100+ years. Each dataset was derived from previous evaluations of traits as range shift predictors and consists of a list of focal species, associated species-level traits, and a range shift metric.

This dataset provides information about the number of properties, residents, and average property values for Range View Road cross streets in Ellensburg, WA.

This point feature class contains 81,481 points arranged in a 270-meter spaced grid that covers the Spring Mountains and Sheep Range in Clark County, Nevada. Points are attributed with hydroclimate variables and ancillary data compiled to support efforts to characterize ecological zones.

The U.S. Geological Survey has been characterizing the regional variation in shear stress on the sea floor and sediment mobility through statistical descriptors. The purpose of this project is to identify patterns in stress in order to inform habitat delineation or decisions for anthropogenic use of the continental shelf. The statistical characterization spans the continental shelf from the coast to approximately 120 m water depth, at approximately 5 km resolution. Time-series of wave and circulation are created using numerical models, and near-bottom output of steady and oscillatory velocities and an estimate of bottom roughness are used to calculate a time-series of bottom shear stress at 1-hour intervals. Statistical descriptions such as the median and 95th percentile, which are the output included with this database, are then calculated to create a two-dimensional picture of the regional patterns in shear stress. In addition, time-series of stress are compared to critical stress values at select points calculated from observed surface sediment texture data to determine estimates of sea floor mobility.

The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of bedrock-outcrop outlines from hand-drawn maps.