Graph and download economic data for Regional Price Parities: All Items: Nonmetropolitan Portion for Pennsylvania (PANMPRPPALL) from 2008 to 2023 about rural, PPP, PA, all items, price, and USA.

This dataset provides information about the number of properties, residents, and average property values for Water Street cross streets in Rural Valley, PA.

About this dataset

Context

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

Key observations

There is a slight majority of male population, with 51.27% of total population being male. 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 Rural Valley is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Rural Valley 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 Rural Valley Population by Race & Ethnicity. You can refer the same here

Abstract Faced with an increasingly globalized and competitive economic market, the formation of solidarity economies and cooperation practices are plausible opportunities for small companies’ development to maintain competitiveness and survival, becoming a path not only to economic but also to human and social development. Therefore, this research aimed to study what competitive gains can be provided to small producers of cassava flour in the village of Manoel dos Santos, a rural area of the municipality of Tracuateua, PA, if they constituted a worker cooperative. The profile of cooperation between producers and the economic environment in case the cooperation takes place was studied, characterizing this research as exploratory with a qualitative methodology. For data collection, the observer researcher and the participant researcher were used as means to carry out interviews, guided by a semi-structured questionnaire and adopting discourse analysis to formulate the results. It was concluded that the constitution of the work cooperative becomes viable, as it can provide the four competitive gains studied (access to solutions, learning and innovation, risk and cost reduction, and social relations). In addition to the results presenting a positive projection of future economic gains with the installed cooperative, most producers are characterized as engaged and committed to the project. This study becomes relevant as it demonstrates that academic knowledge can transform a group of small individual farmers, providing possible alternatives for income generation and social development.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Rural Valley. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Rural Valley, the median household income for the households where the householder is White increased by $2,086(3%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $69,477 in 2013 and $71,563 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 Rural Valley.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-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 Rural Valley median household income by race. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Main Street cross streets in Rural Valley, PA.

BackgroundThe urbanization process may affect the lifestyle of rural residents in China. Limited information exists on the extent of sedentarism and physical activity (PA) level of rural residents in middle-income countries. This is the first survey on sedentary time (ST) and PA among rural residents in eastern China.MethodsThis cross-sectional observational study randomly samples rural adults from Zhejiang Province in eastern China (n = 1,320). Participants' ST and PA levels were determined from the International Physical Activity Questionnaire Short Form through face-to-face interviews, and the influencing factors of PA levels were assessed through multi-class logistic regression analysis.ResultsThe findings showed that the daily ST of the participants ranged from 30 to 660 min, with a median of 240 min (P25, P75:120, 240 min), and 54.6% of participants were sedentary for 240 min or above. The daily ST in men, people aged 18 to 44 years, people with bachelors' degree and above, people working for government agencies or institutions, people with unmarried status, and people with an average income of < 2,000 Yuan was longer than that of other respective groups (p < 0.01). In contrast, the daily ST of people with hypertension or with patients with osteoporosis or osteopenia was less than that of normal people (p < 0.01). Additionally, 69.4% of participants generally had a low level of PA (LPA). Compared with those living in northern Zhejiang, people living in southern Zhejiang who were aged 18–44 years, had bachelor's degree or above, were farmers, and had household incomes below 10,000 Yuan per month were more likely to engage in LPA compared to people > 60 years, with high school or technical education levels or with junior college degrees, working in government agencies and institutions, and with household income above 10,000 Yuan per month (p < 0.05). Furthermore, there was no correlation between ST and PA levels.ConclusionMost rural residents in the Zhejiang Province of eastern China had longer daily ST and a LPA. This was predominant in men, young people, highly educated people, unmarried people, and middle to high-income people. Health education programs should be targeted toward specific population groups to decrease the ST and increase PA.

Population by county for 2000, 2010, and 2020.

This dataset lists all public transit services and reduced-fare programs currently provided in each county of PA.

Public transportation is available in every county in Pennsylvania, with a wide range of services including: - Fixed-route transit service in Philadelphia, Pittsburgh, 21 small urban areas, and 22 rural areas - 44 systems offering shared-ride services in all Pennsylvania counties - 13 intercity bus routes - Keystone Corridor Amtrak service, running from Harrisburg to New York by way of Philadelphia, and Pennsylvanian Amtrak service running from Pittsburgh to Philadelphia - 66 counties with rural transportation for persons with disabilities

NOTE: Service providers offering two or more reduced-fare programs have duplicate records to reflect the various programs and to allow sorting by program type.

County Ag Census Profile Includes:PA CountyChange in Acres 2017 to 2022Number of FarmsNet Average IncomeTop Three Agriculture Products

Panama PA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 1.573 % in 2010. This stayed constant from the previous number of 1.573 % for 2000. Panama PA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 1.573 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 1.573 % in 2010 and a record low of 1.573 % in 2010. Panama PA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

This dataset provides information about the number of properties, residents, and average property values for Jefferson Street cross streets in Rural Valley, PA.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Rural Valley. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Rural Valley. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Rural Valley, the median household income stands at $87,083 for householders within the 45 to 64 years age group, followed by $77,125 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $57,708.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific 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 Rural Valley median household income by age. You can refer the same here

Sustainable transport policy and people living in rural areas : a policy paper is a book. It was written by John Hugh Farrington and published by Scottish Consumer Council in 1996.

Abstract Rural communities that were founded based on land occupation in the Amazon usually have a wide knowledge about the use of medicinal plants. Studies carried out in this context reveal a vast heterogeneity in terms of knowledge and use of medicinal plants. They also describe the influence these communities have on plant diversity and the cultural aspects associated with their use. This paper shows the dynamics of knowledge and use of medicinal plants in the Paulo Fonteles Rural Settlement, established in 2006 in the Mosqueiro District, Belém Municipality, Pará state, Brazilian Amazon. Data was collected through semi-structured interviews and participatory workshops and analyzed qualitatively and quantitatively. Medicinal plants are crucial resources for farmers before and after land occupation. A total of 140 ethnospecies were recorded, of which 119 were identified; They belong to 58 botanical families. 60 species are native to Brazil. Out of those, 21 belong to the phytogeographic domain restricted to Amazonia. 59 species were introduced the region. The local knowledge of medicinal plants is well spread among informants of different age and sex groups. People from other regions in Brazil reported significantly more medicinal plants than those native to the state of Pará. Among the 140 plants mentioned, 110 refer to species whose uses have been maintained over time by informants. The socio-cultural heterogeneity and the dynamic livelihood of the farmers has been crucial for the improvement of their knowledge and the increase of the diversity of the local flora.

*USE geoid20 TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV*The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.Blocks (Census Blocks or Tabulation Blocks) are statistical areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads. Generally, blocks are small in area; for example, a city block bounded on all sides by streets. Blocks in suburban and rural areas may be larger, more irregular in shape, and bounded by a variety of features, such as roads, streams, and transmission lines. In remote areas, blocks may even encompass hundreds of square miles. Blocks cover the entire territory of the United States, Puerto Rico, and the Island Areas. Blocks nest within all other tabulated census geographic entities at the time of the decennial census and are the basis for all tabulated data from that census. Census Block Numbers—Blocks are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are intended to include only water area, but not all water-only blocks have block numbers beginning with 0 (zero).Downloaded from https://www2.census.gov/geo/tiger/TIGER2020/TABBLOCK20 on May 17, 2023

This dataset provides information about the number of properties, residents, and average property values for Rural Avenue cross streets in Williamsport, PA.

The PA_Rural_Health_Clinics_2016 layer contains the latitude and longitude coordinates of 72 rural health clinics in Pennsylvania. When possible, efforts were made to confirm the rooftop location of each rural health clinic. The accuracy of geocoding is available in Geocoding Certainty attribute field (Geocoding Certainty: Rooftop="00", Street="01", Zip Centroid="04", Not geocoded="99"). Latitude and longitude fields are recorded in the WGS 1984 coordinate system.Last updated: 11/09/2016Contact Us: Pennsylvania Department of HealthDivision of Health InformaticsRA-DHICONTACTUS@pa.gov717-782-2448

© Division of Health Informatics This layer is a component of DepHealth.

This layer features:PA CountiesAge Cohorts 2022Gender 2022Race 2022

Includes:PA County BoundaryTable with Birth and Death Rate from 1990 to 2021 per County