Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

About this dataset

Context

The dataset presents the median household income across different racial categories in Au Sable charter township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Au Sable charter township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 94.09% of the total residents in Au Sable charter township. Notably, the median household income for White households is $46,614. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $46,614.

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Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Au Sable charter township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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 Au Sable charter township median household income by race. You can refer the same here

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

Background: Australia has a high proportion of migrants with an increasing migration rate from India. Type II diabetes is a long-term condition common amongst the Indian population.Aims: To investigate patients’ medication-taking behaviour and factors that influence adherence at the three phases of adherence.Methods: Semi-structured interviews were conducted with a convenience sample of 23 Indian migrants living in Sydney. All interviews were audio-recorded, transcribed verbatim and thematically analysed.Results: 1) Initiation: The majority of participants were initially prescribed oral antidiabetic medicine and only two were started on insulin. Most started taking their medicine immediately while some delayed initiating therapy due to fear of side-effects. 2) Implementation: Most participants reported taking their medicine as prescribed. However, some reported forgetting their medicine especially when they were in a hurry for work or were out for social events. 3) Discontinuation: A few participants discontinued taking their medicine. Those who discontinued did so to try Ayurvedic medicine. Their trial continued for a few weeks to a few years. Those who did not receive expected results from the Ayurvedic medicine restarted their prescribed conventional medicine.Conclusion: A range of medication-taking behaviours were observed, ranging from delays in initiation to long-term discontinuation, and swapping of prescribed medicine with Ayurvedic medicine. This study highlights the need for tailored interventions, including education, that focus on factors that impact medication adherence from initiation to discontinuation of therapy.

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This dataset contains Copy Number Variation breakpoint data of YRI, CEU, Ashkenazi Jews I, Ashkenazi Jews II, China, Tibet, India, JPT, Australia, New World, and Taiwan populations.

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