Exercise data set for the SAS book by Uehlinger. Sample of individual variables and cases from the data set of ZA Study 0757 (political ideology). Topics: most important political problems of the country; political interest; party inclination; behavior at the polls in the Federal Parliament election 1972; political participation and willingness to participate in political protests. Demography: age; sex; marital status; religious denomination; school education; interest in politics; party preference. Übungsdatensatz zum SAS-Buch von Uehlinger. Auswahl einzelner Variablen und Fälle aus dem Datensatz der ZA-Studie 0757 (Politische Ideologie). Themen: Wichtigste politische Probleme des Landes; politisches Interesse; Parteineigung; Wahlverhalten bei der Bundestagswahl 1972; politische Partizipation und Teilnahmebereitschaft an politischen Protesten. Demographie: Alter; Geschlecht; Familienstand; Konfession; Schulbildung; Politikinteresse; Parteipräferenz. Random selection Zufallsauswahl Oral survey with standardized questionnaire

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

Survey data the agency uses to track changes in public attitude, knowledge, and behavior related to occupant protection. The MVOSS also collects information related to Emergency Medical Services and crash experience. The survey is composed of two questionnaires, with one focusing on seat belt use and the other focusing on child occupant protection.

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

In general, the stakes are people, property, activities, elements of cultural or environmental heritage, threatened by hazards and likely to be affected or damaged by it. The sensitivity of #x27; an issue to a hazard is called “vulnerability”. This class d' object includes all the issues that have been taken into account in the PPR study. An issue is a dated object whose consideration is based on the object of the PPR and its vulnerability to the hazards studied. A PPR issue can therefore be taken into account (or not) depending on the type or types of hazard treated. These elements form the basis of knowledge of the soil required for the Â#x27; development of the PPR, in the zone d’#x27; study or near it, at the date of #x27; analysis of the issues. The data of #x27; Issues represent a photograph (fixed and not exhaustive) of assets and people exposed to hazards at the time of #x27; development of the risk prevention plan. This data is not updated after PPR approval. In practice they are no longer used: the stakes are recalculated as needed with up-to-date data sources.

Origin

Geometric modelling depends on the type of l' challenge and its modeling in the data source or geographical repository from which it comes. A stake is usually represented by a polygon, a polyline, or a point with a spatial resolution sufficient to appreciate s' it is concerned by one or more zones of #x27;aira.

Partner organisations

DDTM Pas-de-Calais

Links annexes

• XML metadata view

See this page on geo.data.gouv.fr

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

The Pedestrian Crash Data Study (PCDS) collected detailed data on motor vehicle vs pedestrian crashes.

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.This study sought to examine any major changes in schools in the past two years as an evaluation of the Safe and Civil Schools Initiative. Students, faculty, and administrators were asked questions on topics including school safety, climate, and the discipline process.This collection includes 6 SAS data files: "psja_schools.sas7bdat" with 66 variables and 15 cases, "psja_schools_v01.sas7bdat" with 104 variables and 15 cases, "psja_staff.sas7bdat" with 39 variables and 2,921 cases, "psja_staff_v01.sas7bdat" with 202 variables and 2,398 cases, "psja_students.sas7bdat" with 97 variables and 4,382 cases, and "psja_students_v01.sas7bdat" with 332 variables and 4,267 cases. Additionally, the collection includes 1 SAS formats catalog "formats.sas7bcat", and 10 SAS syntax files.

The table presents the percentage of problems where SAS-Pro performed better than, or at par with CE, SSM, and STSA. In addition, the table presents the average improvement in the RMSD, SI, SAS scores for these problems when SAS-Pro is used instead of other solvers.

Tire Pressure Special Study (TPSS) was conducted in order to obtain data to draft FMVSS 138, NVS-432.

IntroductionWe describe transition of HIV-positive children from efavirenz- or nevirapine-based antiretroviral therapy (ART) to optimal dolutegravir (DTG) or lopinavir/ritonavir (LPV/r) (solid formulation)-based ART in Lesotho.MethodsWe followed a cohort of children less than 15 years of age who were initiated on ART on or after January 1, 2018 from 21 selected health facilities in Lesotho. From March 2020 to May 2022, we collected data retrospectively through chart abstraction and prospectively through caregiver interviews to cover a period of 24 months following treatment initiation. We used a structured questionnaire to collect data on demographics, ART regimen, drug formulations and switches, viral suppression, retention, and drug administration challenges. Data were summarized as frequencies and percentages, using SAS ver.9.4.ResultsOf 310 children enrolled in the study, 169 (54.5%) were female, and median age at ART initiation was 5.9 years (IQR 1.1–11.1). During follow-up, 19 (6.1%) children died, 41 (13.2%) were lost to follow-up and 74 (23.9%) transferred to non-study sites. At baseline, 144 (46.4%) children were receiving efavirenz-based ART regimen, 133 (42.9%) LPV/r, 27 (8.7%) DTG, 5 (1.6%) nevirapine; 1 child had incomplete records. By study end, 143 (46.1%) children were receiving LPV/r-based ART regimen, 109 (35.2%) DTG, and 58 (18.7%) were on efavirenz or nevirapine-based regimen. Of 116 children with viral load results after six months or more on a consistent regimen, viral suppression was seen in 35/53 (66.0%) children on LPV/r, 36/38 (94.7%) children on DTG and 19/24 (79.2%) children on efavirenz.ConclusionFollowing optimal ART introduction in Lesotho, most children in the cohort were transitioned and many attained or maintained viral suppression after transition; however, we recommend more robust viral load monitoring and patient tracking to reduce losses and improve outcomes after ART transition.

The 2008 National Survey of Drinking and Driving Attitudes and Behaviors was composed of a single questionnaire administered to a sample of randomly selected individuals 16 and older, with ages 16 through 24 over-sampled. The respondents were asked about their drinking behavior, their drinking and driving behavior, use of designated drivers, their hosting events in which drinking occurred, risks they perceive associated with drinking and driving, experience with anti-DWI enforcement activity, and their attitudes concerning major intervention strategies.The survey was administered from September 10, 2008 to December 22, 2008. A total of 6,999 respondents completed the survey, including 5,392 landline interviews and 1,607 cell phone interviews. The total number of completed interviews for each of the four Census regions (Northeast, Midwest, South, and West) was 1,409, 1,654, 2,390, and 1,546, respectively.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Provide information on the impact of LATCH on child seat use. It will show if consumers are using LATCH to install child safety seats, if they are easy to install and if they are installed correctly.

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

The Risk Prevention Plans (PPR) were established by the Act of 2 February 1995 on strengthening the protection of the environment. They are the key instrument of the State in the field of risk prevention. Their objective is to monitor development in areas exposed to a major risk. The PPRs are approved by the prefects and generally carried out by the departmental directorates of the territories (DDT). These plans regulate land use or land use through building prohibitions or requirements on existing or future buildings (constructive provisions, vulnerability reduction work, restrictions on agricultural use or practices, etc.). These plans may be under development (prescribed), implemented in advance or approved. The RPP file contains a presentation note, a regulatory zoning plan and a regulation. Other graphic documents that are useful for understanding the approach (e.g. hazards, issues, etc.) can be attached. Each PPR is identified by a polygon that corresponds to the set of affected municipalities within the scope of the prescription when it is in the prescribed state; and the envelope of restricted areas when it is in the approved state. This geographical table allows mapping existing PPRNs or PPRTs on the department.

Abstract (en): This study is part of a time-series collection of national surveys fielded continuously since 1952. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. A Black supplement of 263 respondents, who were asked the same questions that were administered to the national cross-section sample, is included with the national cross-section of 1,571 respondents. In addition to the usual content, the study contains data on opinions about the Supreme Court, political knowledge, and further information concerning racial issues. Voter validation data have been included as an integral part of the election study, providing objective information from registration and voting records or from respondents' past voting behavior. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. United States citizens of voting age living in private households in the continental United States. A representative cross-section sample, consisting of 1,571 respondents, plus a Black supplement sample of 263 respondents. 2015-11-10 The study metadata was updated.1999-12-14 The data for this study are now available in SAS transport and SPSS export formats, in addition to the ASCII data file. Variables in the dataset have been renumbered to the following format: 2-digit (or 2-character) year prefix + 4 digits + [optional] 1-character suffix. Dataset ID and version variables have also been added. In addition, SAS and SPSS data definition statements have been created for this collection, and the data collection instruments are now available as a PDF file. face-to-face interview, telephone interviewThe SAS transport file was created using the SAS CPORT procedure.