There were approximately **** thousand employees in national parks and other nature institutions in the United States in 2023. This shows an increase of *** percent over the previous year. This figure was forecast to grow again in 2024.

The National Prisoner Statistics (NPS) data collection began in 1926 in response to a congressional mandate to gather information on persons incarcerated in state and federal prisons. Originally under the auspices of the United States Census Bureau, the collection moved to the Bureau of Prisons in 1950, and then in 1971 to the National Criminal Justice Information and Statistics Service, the precursor to the Bureau of Justice Statistics (BJS) which was established in 1979. Since 1979, the Census Bureau has been the NPS data collection agent. The NPS is administered to 51 respondents. Before 2001, the District of Columbia was also a respondent, but responsibility for housing the District of Columbia's sentenced prisoners was transferred to the federal Bureau of Prisons, and by yearend 2001 the District of Columbia no longer operated a prison system. The NPS provides an enumeration of persons in state and federal prisons and collects data on key characteristics of the nation's prison population. NPS has been adapted over time to keep pace with the changing information needs of the public, researchers, and federal, state, and local governments.

Survey response rates by national park and park-related information.

In financial year 2022, there were over five million state government employees under the National Pension System Trust. In 2004, the NPS was rolled out by the Indian government and became the only universal scheme to handle assets and funds of all subscribers.

Mean and median knowledge scores by national park.

This feature layer contains points collected by Glacier National Park (Montana) Citizen Science volunteers and staff to document mountain goat and bighorn sheep detections and distribution from 2022- present. This Citizen Science form enables volunteers and park staff to record mountain goat and bighorn sheep population data while in the field. It includes questions regarding survey conditions (weather, date, number of people present, etc.), document a geopoint for observer location as well as sheep and goat location, equipment used, other wildlife seen in survey areas, and an exit page inquiring about time to complete survey and hiking distance. This survey is intended for use within Glacier National Park, Montana. This project was created using Survey123 and is managed by the Glacier National Park Citizen Science team in the Crown of the Continent Research Learning Center. The resulting data from submitted surveys is used to compile final reports regarding mountain goat and bighorn sheep baseline population estimates, population trends, and geographic distribution. Data is collected by citizen science volunteers and student and service groups. Data is QA/QC’d by Glacier National Park Citizen Science staff and backed up monthly.The corresponding NPS DataStore on Integrated Resource Management Applications (IRMA) reference is Glacier National Park Mountain Goat and Bighorn Sheep Citizen Science

In financial year 2022, there were around *** million unorganized sector employees who were subscribed under the National Pension System Trust. The NPS was rolled out by the Indian government in 2004, and became the only universal scheme to handle assets and funds of all subscribers.

Mean and median practice scores by national park.

Percent of individuals choosing correct knowledge section responses by park.

Abstract

The recent global economic slowdown, caused by the COVID-19 pandemic, created an urgent need for timely data to monitor the socioeconomic impacts of the pandemic. Tanzania is among other countries in the world which are affected by the recent global economic slowdown, caused by the COVID-19 pandemic. Therefore, there is an urgent need for timely data to monitor and mitigate the socio-economic impacts of the crisis in the country. Responding to this need, the National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS), Zanzibar in collaboration with the World Bank and Research on Poverty Alleviation (REPOA) implemented a rapid household telephone survey called the Tanzania High-Frequency Welfare Monitoring Survey (HFWMS).

Thus, the main objective of the survey is to obtain timely data that is critical for evidence-based decision making aimed at mitigating the socio-economic impact of the downturn caused by COVID-19 pandemic by filling critical gaps of information that can be used by the government and stakeholders to help design policies to mitigate the negative impacts on its population.

Geographic coverage

National

Analysis unit

Households Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary sample for this activity was drawn from the 2014/15 NPS and 2017/18 HBS. Target sample completion each month is estimated at 3000 households. The 2014/15 NPS acted as the primary sample frame, complimented by the 2017/18 HBS.

The sample for the HFWMPS was drawn from the 2014/15 NPS and 2017/18 HBS. Both surveys were conducted over a 12-month period and are nationally representative. During the implementation of the surveys, phone numbers are collected from interviewed households and reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and these households made this an ideal frame from which to conduct the HFWMS in Tanzania.

To obtain a nationally representative sample for the Tanzania HFWMS, a sample size of approximately 3,000 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Thus, about 5,750 households were selected to be contacted.

All 5,750 households were contacted in the baseline round of the phone survey. [Error! Reference source not found. ] presents the interview result for the baseline sample. 49.2 percent of sampled households were successfully contacted. Of those contacted, 96 percent or 2,708 households were fully interviewed. These 2,708 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Each survey round consists of one questionnaire - a Household Questionnaire administered to all households in the sample.

Baseline The questionnaire gathers information on demographics; employment; education; access to basic services; food security; TASAF; and mental health. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Education: School attendance, type of school attended, learning activities of children at home, return to school, contact with children’s teachers during school closure.
• Access to Basic Services:Household’s access to staple food (maize grain, cassava, rice, and maize flour), medical treatment, and reasons for not being able to access the services.
• Food Security: Household’s food security status during the last 30 days.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Mental Health: Information on 8 items pertaining to measuring mental health.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 2 The questionnaire gathers information on demographics; employment; non-farm enterprise; tourism; education; access to health services; and TASAF. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Tourism: Employment of household members in tourism sector, and who benefits from tourism.
• Education (selected members aged 4-18 years): School attendance, reason for not attending, grade attending, type of school, absence and reason for being absent.
• Access to Health Services: Women’s access to pre-natal/post-natal care, household’s access to preventative care and medical treatment, and reasons for not being able to access the services.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview

Round 3 The questionnaire gathers information on demographics; employment (respondent and other household members); non-farm enterprise; credit; women savings; and shocks and coping. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Employment (other members): Status in employment (current and 2020), consistency of work in 2020, why currently not working, job search, change in jobs, actual job.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Credit: Household’s debts status since the beginning of the coronavirus crisis; use of loan, ability to repay loan when their scheduled payment is due.
• Women Savings: Women having bank accounts to financial institutions and changes in their savings since the start of the pandemic.
• Shocks and Coping: Shocks that affected household since the baseline interview and their coping strategies.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 4 The questionnaire gathers information on demographics; employment; non-farm enterprise; digital technology; and income changes. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH3-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.