United States Immigrants Admitted: All Countries data was reported at 1,127,167.000 Person in 2017. This records a decrease from the previous number of 1,183,505.000 Person for 2016. United States Immigrants Admitted: All Countries data is updated yearly, averaging 451,510.000 Person from Sep 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 1,827,167.000 Person in 1991 and a record low of 23,068.000 Person in 1933. United States Immigrants Admitted: All Countries data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

Public use data set on new legal immigrants to the U.S. that can address scientific and policy questions about migration behavior and the impacts of migration. A survey pilot project, the NIS-P, was carried out in 1996 to inform the fielding and design of the full NIS. Baseline interviews were ultimately conducted with 1,127 adult immigrants. Sample members were interviewed at baseline, 6 months, and 12 months, with half of the sample also interviewed at three months. The first full cohort, NIS-2003, is based on a nationally representative sample of the electronic administrative records compiled for new immigrants by the US government. NIS-2003 sampled immigrants in the period May-November 2003. The geographic sampling design takes advantage of the natural clustering of immigrants. It includes all top 85 Metropolitan Statistical Areas (MSAs) and all top 38 counties, plus a random sample of other MSAs and counties. Interviews were conducted in respondents'' preferred languages. The baseline was multi-modal: 60% of adult interviews were administered by telephone; 40% were in-person. The baseline round was in the field from June 2003 to June 2004, and includes in the Adult Sample 8,573 respondents, 4,336 spouses, and 1,072 children aged 8-12. A follow-up was planned for 2007. Several modules of the NIS were designed to replicate sections of the continuing surveys of the US population that provide a natural comparison group. Questionnaire topics include Health (self-reports of conditions, symptoms, functional status, smoking and drinking history) and use/source/costs of health care services, depression, pain; background; (2) Background: Childhood history and living conditions, education, migration history, marital history, military history, fertility history, language skills, employment history in the US and foreign countries, social networks, religion; Family: Rosters of all children; for each, demographic attributes, education, current work status, migration, marital status and children; for some, summary indicators of childhood and current health, language ability; Economic: Sources and amounts of income, including wages, pensions, and government subsidies; type, value of assets and debts, financial assistance given/received to/from respondent from/to relatives, friends, employer, type of housing and ownership of consumable durables. * Dates of Study: 2003-2007 * Study Features: Longitudinal * Sample Size: 13,981

This is a collection of data from the U.S. Customs and Border Protection (CBP), primarily about apprehensions of illegal immigrants. All the data here was originally scraped from the PDFs available from the U.S. Customs and Border Protection's Stats and Summaries page on their website (https://www.cbp.gov/newsroom/media-resources/stats). I am in no way affiliated with CBP. What I did was take their public files and scrape the tables in the PDFs to make them more accessible. I then combined some tables together and reshaped the data to make it easier to use for analysis. The data is now available in R, Stata, and Excel (.csv) formats. Please note that all the data is in fiscal years (October-September), not in calendar years. There are 8 files and they all contains different information and some contains different number of years. Below is the file name, a brief description, the years of data available, and which variables it has, for each file. Apprehensions and Seizures Statistics 2011-2019 This provides the annual number of seizures of drugs and weapons for aggregate border sectors between the years 2011 and 2019. Drug data also includes the amount of drugs seized (in pounds for all but heroin which is measured in ounces). Sector Fiscal year Apprehensions from a special interest country Rounds of ammunition Total apprehensions Pounds of cocaine Number of cocaine seizures Conveyances Currency (in dollars) Pounds of ecstasy Number of ecstasy seizures Number of firearms Ounces of heroin Number of heroin seizures Pounds of marijuana Number of marijuana seizures Pounds of meth Number of meth seizures Pounds of other drugs (not cocaine, heroin, ecstasy, or marijuana) Number of other drugs seizures ((not cocaine, heroin, ecstasy, or marijuana) Apprehensions of illegal immigrants from countries other than Mexico Border Patrol Staffing 1992-2019 The annual number of agents employed for each sector in the country between 1992 and 2019. Sector Fiscal year Number of agents Family, Unaccompanied Children, and Total Apprehensions 2000-2019 The monthly number of total apprehensions, family apprehensions (defined as "the number of individuals (either a child under 18 years old, parent, or legal guardian) apprehended with a family member by the U.S. Border Patrol."), or unaccompanied children (person under the age 18 traveling alone (without a family member)) for each sector in the country. Total apprehension data is available for the years 2000 to 2019. Family apprehension data is available for the years 2013 to 2019. Unaccompanied children data is available for the years 2010 to 2019. Sector Fiscal year Month Total apprehensions Unaccompanied children apprehensions (fiscal years 2010-2019 only) Family apprehensions (fiscal years 2013-2019 only) Other than Mexico Apprehensions 2000-2019 The annual number of apprehensions for each sector in the country between 2000 and 2019. This also includes the annual number of apprehensions of immigrants from Mexico and the number of illegal immigrants from countries other than Mexico. Sector Fiscal year Total apprehensions Total apprehensions of illegal immigrants from Mexico Total apprehensions of illegal immigrants from countries other than Mexico Nationwide Total Apprehensions 1925-2019 This is a simple count of how many apprehensions of illegal immigrants were made in the United States each year between 1925 and 2019. Fiscal year Total apprehensions Sector Profile 2011-2019 This provides a number of various variables for each sector in the country between the years 2011 and 2019. Sector Fiscal year Number of agents Total apprehensions Apprehensions of illegal immigrants from countries other than Mexico Pounds of marijuana Pounds of cocaine Number of accepted prosecutions Number of assaults Number of rescues (Southwest border only) Number of deaths (Southwest border only) Number of accompanied children (under age 18) apprehended Number of unaccompanied children (under age 18) apprehended Total number of children (under age 18) apprehended Total number of adults (age 18 or older) apprehended Total number of females apprehended Total number of males apprehended Southwest Border Apprehensions 1960-2019 The annual number of apprehensions for each sector in the Southwest border between 1960 and 2019. Sector Fiscal year Total apprehensions Southwest Border Deaths 1998-2019 The annual number of deaths for each sector in the Southwest border between 1998 and 2019. Sector Fiscal year Deaths Some of the data sets present the data broken into border sectors. Below is a list of the three borders and the sectors each one includes. Coastal Border Livermore (closed after fiscal year 2004) Miami New Orleans Ramey Northern Border Blaine Buffalo Detroit Grand Forks Havre Houlton Spokane Southwest Border Big Bend Del Rio El Centro El Paso Laredo Rio Grande Valley San Diego Tucson Yuma

ABSTRACT

Statements about building walls, deportation and denying services to undocumented immigrants made by the US president may induce fear in Latino populations and create barriers to their health care access. To assess the impact of these statements on undocumented Latino immigrants' (UDLI) and Latino legal residents/citizens' (LLRC) perceptions of safety and their presentations for emergency care, we conducted surveys of adult patients at three county emergency departments (EDs) in California from June 2017 to December 2018. Of 1,684 patients approached, 1,337 (79.4%) agreed to participate: 34.3% UDLI, 36.9% LLRC, and 29.8% non-Latino legal residents/citizens (NLRC). The vast majority of UDLI (95%), LLRC (94%), and NLRC (85%) had heard statements about immigrants by President Trump. Most UDLI (89%), LLRC (88%), and NLRC (87%) either thought that these measures were being enacted now or will be enacted in the future. Most UDLI and LLRC reported that these statements made them feel unsafe living in the US, 75% (95% CI 70 to 80%) and 51% (95% CI 47 to 56%), respectively. More UDLI reported that these statements made them afraid to come to the ED (24% 95% CI 20 to 28%) vs LLRC (4.4% [95% CI 3 to 7%]) and NLRC (3.5% [95% CI 2 to 6%]); 55% of UDLI with this fear stated it caused them to delay coming to the ED (median delay 2-3 days). The vast majority of patients in our California EDs have heard statements about immigrants by the US president, which have induced worry and safety concerns in both UDLI and LLRC patients. These statements may also act as a barrier to some UDLI's access of emergency care. Given California's sanctuary state status, these safety concerns and ED access fears may be greater in a nationwide population of Latinos.

This research adds to the expanding field of data-driven analysis, scientific modeling, and forecasting on the impact of having access to the Internet and IoT on the general US population regarding immigrants and immigration policies. More specifically, this research focuses on the public opinion of undocumented immigrants in the United States and having access to the Internet in their local settings. The term Undocumented Immigrants refers to those who live in the United States without legal papers, documents, or visas. Undocumented immigrants may have come into the country unlawfully or with valid documentation, but their legal status has expired. Using the 2020 American National Election Studies (ANES) time series dataset, I investigated the relationship between internet access (A2I) and public perception of undocumented immigrants. According to my research and analysis, increasing internet access among non-Hispanic whites with at least a bachelors degree with an annual household income of less than 99K is more likely to oppose the deportation of undocumented immigrants and separating unaccompanied children from their families in borderland areas. The individuals with substantial Republican political ideology exhibit significantly lower opposing effects in deporting undocumented immigrants or separating unaccompanied children from their families. The evidence from multiple statistical models is resilient to a variety of factors. The findings show that increased internet access may improve undocumented immigrants social integration and acceptability. During health emergencies, it may be especially beneficial to make them feel safe, included, and supported in their local settings.

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

A comprehensive dataset of Green Card sponsorship trends, including data on employment-based Green Cards, labor certifications, top employers, and industry trends.

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