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} }

The simulated synthetic aperture sonar (SAS) data presented here was generated using PoSSM [Johnson and Brown 2018]. The data is suitable for bistatic, coherent signal processing and will form acoustic seafloor imagery. Included in this data package is simulated sonar data in Generic Data Format (GDF) files, a description of the GDF file contents, example SAS imagery, and supporting information about the simulated scenes. In total, there are eleven 60 m x 90 m scenes, labeled scene00 through scene10, with scene00 provided with the scatterers in isolation, i.e. no seafloor texture. This is provided for beamformer testing purposes and should result in an image similar to the one labeled "PoSSM-scene00-scene00-starboard-0.tif" in the Related Data Sets tab. The ten other scenes have varying degrees of model variation as described in "Description_of_Simulated_SAS_Data_Package.pdf". A description of the data and the model is found in the associated document called "Description_of_Simulated_SAS_Data_Package.pdf" and a description of the format in which the raw binary data is stored is found in the related document "PSU_GDF_Format_20240612.pdf". The format description also includes MATLAB code that will effectively parse the data to aid in signal processing and image reconstruction. It is left to the researcher to develop a beamforming algorithm suitable for coherent signal and image processing. Each 60 m x 90 m scene is represented by 4 raw (not beamformed) GDF files, labeled sceneXX-STARBOARD-000000 through 000003. It is possible to beamform smaller scenes from any one of these 4 files, i.e. the four files are combined sequentially to form a 60 m x 90 m image. Also included are comma separated value spreadsheets describing the locations of scatterers and objects of interest within each scene. In addition to the binary GDF data, a beamformed GeoTIFF image and a single-look complex (SLC, science file) data of each scene is provided. The SLC data (science) is stored in the Hierarchical Data Format 5 (https://www.hdfgroup.org/), and appended with ".hdf5" to indicate the HDF5 format. The data are stored as 32-bit real and 32-bit complex values. A viewer is available that provides basic graphing, image display, and directory navigation functions (https://www.hdfgroup.org/downloads/hdfview/). The HDF file contains all the information necessary to reconstruct a synthetic aperture sonar image. All major and contemporary programming languages have library support for encoding/decoding the HDF5 format. Supporting documentation that outlines positions of the seafloor scatterers is included in "Scatterer_Locations_Scene00.csv", while the locations of the objects of interest for scene01-scene10 are included in "Object_Locations_All_Scenes.csv". Portable Network Graphic (PNG) images that plot the location of objects of all the objects of interest in each scene in Along-Track and Cross-Track notation are provided.

The 1990 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population data from the U.S. Census Bureau's 1990 Summary tape File (STF3A) database. The data are available by state and county, county subdivision/mcd, blockgroup, and places, as well as Indian reservations, tribal districts and congressional districts. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Patient appointment information is obtained from the Veterans Health Information Systems and Technology Architecture Scheduling module. The Patient Appointment Information application gathers appointment data to be loaded into a national database for statistical reporting. Patient appointments are scanned from September 1, 2002 to the present, and appointment data meeting specified criteria are transmitted to the Austin Information Technology Center Patient Appointment Information Transmission (PAIT) national database. Subsequent transmissions (bi-monthly) update PAIT bi-monthly via Health Level Seven message transmissions through Vitria Interface Engine (VIE) connections. A Statistical Analysis Software (SAS) program in Austin utilizes PAIT data to create a bi-monthly SAS dataset on the Austin mainframe. This additional data is used to supplement the existing Clinic Appointment Wait Time and Clinic Utilization extracts created by the Veterans Health Administration Support Service Center (VSSC).

The 1980 SAS Transport Files portion of the Archive of Census Related Products (ACRP) contains housing and population demographics from the 1980 Summary Tape File (STF3A) database and are organized by state. The population data includes education levels, ethnicity, income distribution, nativity, labor force status, means of transportation and family structure while the housing data embodies size, state and structure of housing Unit, value of the Unit, tenure and occupancy status in housing Unit, source of water, sewage disposal, availability of telephone, heating and air conditioning, kitchen facilities, rent, mortgage status and monthly owner costs. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

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 addressed the dearth of information about facilitators of transnational organized crime (TOC) by developing a method for identifying criminal facilitators of TOC within existing datasets and extend the available descriptive information about facilitators through analysis of pre-sentence investigation reports (PSRs). The study involved a two-step process: the first step involved the development of a methodology for identifying TOCFs; the second step involved screening PSRs to validate the methodology and systematically collect data on facilitators and their organizations. Our ultimate goal was to develop a predictive model which can be applied to identify TOC facilitators in the data efficiently.The collection contains 1 syntax text file (TOCF_Summary_Stats_NACJD.sas). No data is included in this collection.

This data set contains a composite of the highest resolution (i.e. the "native" resolution) upper air sounding data from all sources for the Southeast Atmosphere Study (SAS). Sounding data is included from two sources: the National Weather Service (16 sites and 1438 soundings) and the NCAR/EOL ISS GAUS radiosonde site near the SOAS Centreville site in central Alabama (1 site and 105 soundings). Included are soundings from 30 May to 15 July 2013.

Generations of the population from 2000 to 2022 divided by district - The dataset derives from the connection of the information on the populations present in the SAS datawherehouse of the municipality with the information present on the SIT:

This database contains monthly mean surface temperature and mean sea level pressure data from twenty-nine meteorological stations within the Antarctic region. The first version of this database was compiled at the Climatic Research Unit (CRU) of University of East Anglia, Norwich, United Kingdom. The database extended through 1988 and was made available in 1989 by the Carbon Dioxide Information Analysis Center (CDIAC) as a Numeric Data Package (NDP), NDP-032. This update of the database includes data through early 1999 for most stations (through 2000 for a few), and also includes all available mean monthly maximum and minimum temperature data. For many stations this means that over 40 years of data are now available, enough for many of the trends associated with recent warming to be more thoroughly examined. Much of the original version of this dataset was obtained from the World Weather Records (WWR) volumes (1951-1970), Monthly Climatic Data for the World (since 1961), and several other sources. Updating the station surface data involved requesting data from countries who have weather stations on Antarctica. Of particular importance within this study are the additional data obtained from Australia, Britain and New Zealand. Recording Antarctic station data is particularly prone to errors. This is mostly due to climatic extremes, the nature of Antarctic science, and the variability of meteorological staff at Antarctic stations (high turnover and sometimes untrained meteorological staff). For this compilation, as many sources as possible were contacted in order to obtain as close to official `source' data as possible. Some error checking has been undertaken and hopefully the final result is as close to a definitive database as possible. This NDP consists of this html documentation file, an ASCII text version of this file, six temperature files (three original CRU files for monthly maximum, monthly minimum, and monthly mean temperature and three equivalent files slightly reformatted at CDIAC), two monthly mean pressure data files (one original CRU file and one slightly reformatted CDIAC version of the file), four graphics files that describe the station network and the nature of temperature and pressure trends, a file summarizing annual and mean-monthly trends in surface temperatures over Antarctica, a file summarizing monthly Antarctic surface temperature anomalies with respect to the period 1961-90, a station inventory file, and 3 FORTRAN and 3 SAS routines for reading the data that may be incorporated into analysis programs that users may devise. These 23 files have a total size of approximately 2 megabytes and are available via the Internet through CDIAC's Web site or anonymous FTP (File Transfer Protocol) server, and, upon request, various magnetic media. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp032/ndp032.html This dataset was transferred from the CDIAC Archive and published on ESS-DIVE in 2018 under the project title "Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); University of East Anglia, Norwich, United Kingdom". In 2023, the project title was updated to "Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)" to enable consistent management of all datasets previously hosted by the CDIAC Archive that are now published on ESS-DIVE.

Indonesia Big Data Analytics Software Market Analysis The Indonesia Big Data Analytics Software market is poised to witness substantial growth over the forecast period of 2025-2033, with a CAGR of 9.35%. In 2025, the market stood at a value of USD 43.15 million and is projected to reach a remarkable value by 2033. This growth is primarily driven by the increasing adoption of digital technologies, the proliferation of data-intensive applications, and the growing need for businesses to make data-driven decisions. Key trends shaping the market include the rising popularity of cloud-based big data analytics solutions, the emergence of advanced analytics techniques such as machine learning and artificial intelligence, and the growing awareness of data privacy and security concerns. Despite these positive factors, the market faces challenges such as the lack of skilled professionals in data analytics, the high cost of implementation, and the complexities associated with managing and integrating large volumes of data. Prominent players in the market include Teradata, SAS, SAP, Tableau Software, and IBM Corporation, among others. Market Size and Growth The Indonesia Big Data Analytics Software Market is projected to grow from USD 235.6 million in 2023 to USD 1,159.1 million by 2029, exhibiting a CAGR of 24.3% during the forecast period. This growth can be attributed to the increasing adoption of big data analytics solutions by organizations to enhance their decision-making, improve operational efficiency, and gain a competitive advantage. Recent developments include: June 2024: Indosat Ooredoo Hutchison (Indosat) and Google Cloud expanded their long-term alliance to accelerate Indosat’s transformation from telco to AI Native TechCo. The collaboration will combine Indosat’s vast network, operational, and customer datasets with Google Cloud’s unified AI stack to deliver exceptional experiences to over 100 million Indosat customers and generative AI (GenAI) solutions for businesses across Indonesia. These include geospatial analytics and predictive modeling, real-time conversation analysis, and back-office transformation. Indosat’s early adoption of an AI-ready data analytics platform exemplifies its forward-thinking approach., June 2024: Palo Alto Networks launched a new cloud facility in Indonesia, catering to the rising demand for local data residency compliance. The move empowers organizations in Indonesia with access to Palo Alto Networks' Cortex XDR advanced AI and analytics platform that offers a comprehensive security solution by unifying endpoint, network, and cloud data. With this new infrastructure, Indonesian customers can ensure data residency by housing their logs and analytics within the country.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP released PAD-US 3.0 Statistics and Reports in the summer of 2022. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 3.0 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 51,000 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform.

The Hamby 6000 rig
Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560

Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options.

The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply.

Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson.

Experimental design and operational details of research conducted are contained in related published articles, however a further description of the measured data signals and commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically, this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120 cm from and flush facing a titanium dioxide white painted panel, a normalized unity value of 1.0 can be set for each detector. To generate this dataset, we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

Noting that this type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does not represent a reflection of the plant material solely, because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However, the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are easier to direct, and in our capture method we target a consistent sensor height that is 1 m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost, multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used supplying data in a lined format, where variables are repeated for each sensor, creating a discrete data row for each individual sensor measurement instance.

We offer five high-throughput single pixel spectral colors, recorded at 530, 550, 590, 670, 730, and 800nm (NIR). The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including increased noise). Importantly, two green frequencies are available in this study, which is different from the alternate focus on the other side of the spectrum in the first two Bronson Files datasets measuring cotton.

Dual, or tandem, CropCircle sensor paired usage empowers additional vegetation index calculations such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

On collection 7/28/2014 and thereafter, a new HS logger, the GeoScoutX, or GSX, was initiated. The upgraded data recorder increased operational reliability by eliminating recording stops and subsequent multiple data files. The new data outputs were defined by the operating system configuration version where the data variables column headers were changed to be named SF00, SF01, SF02, SF03 and SF04. The raw reflectance columns are the first three, SF00-02, and the last two columns are the onboard calculated VIs, which we did not consider.

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

This second data component, expanding measurement using Campbell Scientific records and additional sensors was added during the season. However unfortunate, the CS data of this dataset is of poor quality. The IRT sensors that were

This CD consists of a series of data files and SAS and SPSS code files containing the Public Use Microdata Sample L. It was produced by the U.S. Bureau of the Census under contract with the Louisiana Population Data Center, LSU Agricultural Center. PUMS-L contains a unique labor market area (LMA) geography delineated by Charles M. Tolbert (LSU) and Molly Sizer (University of Arkansas). PUMS-L is a minimum 0.25 percent sample. Like all PUMS geographic units, the labor market areas must have a population of at least 100,000 persons. To avoid having as few as 250 cases in smaller LMAs, the Bureau made an effort to supply at least 2000 person records per LMA. Inclusion of these additional person records resulted in a 0.45 percent sample. Sampling weights are included in the file that compensate for this oversampling of smaller LMAs. The resulting file contains information on 519,237 households and 1,139,142 persons. Weighted totals are: households - 101,916,857, persons - 248,709,867. This CD-ROM edition of PUMS-L was prepared and mastered by the Louisiana Population Data Center. The files on this CD-ROM are organized in several directories. These directories contain raw PUMS-L data files, equivalency files that document the labor market area geography, Atlas Graphics files that can be used to produce maps, and compressed, rectangularized SAS and SPSS-PC system files. One of the SAS files is an experienced civilian labor force extract that may facilitate research on labor market issues. Also included are SAS and SPSS programs configured for PUMS-L.

Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Abstract (en): These data were collected to develop a means of identifying those individuals most likely to be dangerous to others because of their pursuit of public figures. Another objective of the study was to gather detailed quantitative information on harassing and threatening communications to public figures and to determine what aspects of written communications are predictive of future behavior. Based on the fact that each attack by a mentally disordered person in which an American public figure was wounded had occurred in connection with a physical approach within 100 yards, the investigators reasoned that accurate predictions of such physical approaches could serve as proxies for the less feasible task of accurate prediction of attacks. The investigators used information from case files of subjects who had pursued two groups of public figures, politicians and celebrities. The data were drawn from the records of the United States Capitol Police and a prominent Los Angeles-based security consulting firm, Gavin de Becker, Inc. Information was gathered from letters and other communications of the subjects, as well as any other sources available, such as police records or descriptions of what occurred during interviews. The data include demographic information such as sex, age, race, marital status, religion, and education, family history information, background information such as school and work records, military history, criminal history, number of communications made, number of threats made, information about subjects' physical appearance, psychological and emotional evaluations, information on travel/mobility patterns, and approaches made. 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: Standardized missing values.. Individuals who pursue public figures. Only subjects who had written letters or mailed some other type of communication and had been on file for at least six months prior to the beginning of data compilation were included. The subjects were then classified as approach-positive or approach-negative according to six criteria. The investigators controlled for the number of communications in a file so that the approach-positive samples and the approach-negative samples had similar distributions of numbers of communications. Part 1 is a stratified sample, and Part 2 is nonstratified. 2006-03-30 File CB6007.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions.2004-07-26 Data were converted from card image to logical record length, SAS and SPSS data definition statements were created, and documentation was updated and converted to machine-readable format (PDF). Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (83-NI-AX-0005). (1) The documentation for this data collection does not indicate the time period to which the data refer. In addition, users should note that according to the documentation the individuals described in the collection are not representative of any geographic area. (2) The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Dr. Kevin Bronson provides a second year of nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw data sensor records and logger outputs.

This proximal terrestrial high-throughput plant phenotyping data examples our early tri-metric field method, where a geo-referenced 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this development period, our Proximal Sensing Cart Mark1 (PSCM1) platform suspends a single cluster of sensors on a dual sliding vertical placement armature.

Experimental design and operational details of research conducted are contained in related published articles, however further description of the measured data signals as well as germane commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120cm from a titanium dioxide white painted panel, a normalized unity value of 1.0 is set for each detector. To generate this dataset we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

This type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality, and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does however, not represent a reflection of the plant material solely because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are more easy to direct, and in our capture method we target a consistent sensor height that is 1m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version. Fifteen collection events are presented.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost. Multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used, supplying data in a lined format, where variables are repeated for each sensor. This created a discrete data row for each individual sensor measurement instance.

We offer six high-throughput single pixel spectral colors, recorded at 530, 590, 670, 730, 780, and 800nm. The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including an increased noise).

Dual, or tandem approach, CropCircle paired sensor usage empowers additional vegetation index calculations, such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Improved Campbell Scientific data at 5Hz is presented for nine collection events, where thermal, ultrasonic displacement, and additional GPS metrics were recorded. Ultrasonic height metrics generated by the Honeywell sensor and present in this dataset, represent successful phenotypic recordings. The Honeywell ultrasonic displacement sensor has worked well in this application because of its 180Khz signal frequency that ranges 2m space. Air temperature is still a developing metric, a thermocouple wire junction (TC) placed in free air with a solar shade produced a low-confidence passive ambient air temperature.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

There is canopy thermal data from three view vantages. A nadir sensor view, and looking forward and backward down the plant row at a 30 degree angle off nadir. The high confidence Apogee Instruments SI-111 type infrared radiometer, non-contact thermometer, serial number 1022 was in a front position looking forward away from the platform, number 1023 with a nadir view was in middle position, and sensor number 1052 was in a rear position and looking back toward the platform frame. We have a long and successful history testing and benchmarking performance, and deploying Apogee Instruments infrared radiometers in field experimentation. They are biologically spectral window relevant sensors and return a fast update 0.2C accurate average surface temperature, derived from what is (geometrically weighted) in their field of view.

Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost, or rarely on HS GeoScout logger error. GPS information may be missing at the start of data recording. However once the receiver supplies a signal the values will populate. Likewise there may be missing information at the end of a data collection, where the GPS signal was lost but sensors continue to record along with the data logger timestamping.

In the raw CS data, collections 1 through 7 are represented by only one table file, where the UTC from the GPS

Dr. Kevin Bronson provides this dataset representing the first of three consecutive years of cotton and nitrogen management experimentation in Field 113. Included, is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

Experimental design and operational details of research conducted are contained in related published articles, however a further description of the measured data signals and commentary is herein offered.

Typical nitrogen fertilizer was delivered as liquid UAN 32-0-0 fertilizer with a density of 11.1 pounds per gallon, which contains 3.5 pounds of nitrogen per gallon. Notably, subsequent 2017 and 2018 experimentation years include a large volume of depleted nitrogen-15 isotope recovery tracing.

GeoScoutX logging of CropCircle active optical reflectance sensing data -
The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 generated reflectance numbers. Which as derived here, consists of raw active optical band-pass values digitized onboard the sensor product. Data was delivered as sequential serialized text output including the associated GPS information. Typically, this product examples a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. However, we used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product, being not only rugged and reliable, but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120 cm from and flush facing to a titanium dioxide white painted panel, a normalized unity value of 1.0 can be set for each detector. To generate this dataset, we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalization) on each sensor individually, before each data collection, and without the use of any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows PC machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

Noting that this type of raw value active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system. We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically while onboard the “black-box” device, than are other reflectance products which produce vegetation indices that are averages of multiple detector samples.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within the field view. It does not represent a reflection of the plant material solely, because it can contain additional features in the view.

Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI; because the active light source does not transmit energy to penetrate perhaps past LAI 2.1, which is less than what is expected with a solar induced passive reflectance sensor. However, the focus of the active sensor scan is orientated on the uppermost expanded canopy leaves, and those leaves are normally positioned to intercept the major of incoming solar energy. Active energy sensors are easier to direct, where this capture method targets a consistent sensor height of 1 m above the average canopy height, and a roaming travel speed maintained around 1.5 mph, with the sensors parallel to earth in a nadir view.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors that were measured on the GeoScoutX digital propriety serial data logger, have a stable output format as defined by firmware version.

Different numbers of csv data files were generated based on field operations. Raw data files include the inserted null value placeholder -9999. CropCircle sensors supplied data in a lined format, where variables were repeated for each sensor creating a discrete data row for each individual sensor measurement instance.

Hamby rig active optical reflectance data was generated by Holland Scientific CropCircle ACS-470 sensors, which were numbered 1, 2, 4 and 5, where sensors 1 and 5 had band-pass filters centered at nanometer frequency 550, 670, and 530, while sensors 2 and 4 had filters 590, 800, and 730, each for their respective R1, R2, and R3 raw detector data channels. The placement of the filters was determined as a generic optimization, where the longer wave filter was put in the middle detector position, and where the tandem sensor setup was optimized for the favored NDRE on one sensor and a green frequency test configuration on the other. Although when facing forward, there is a left and right side for the two cotton rows measured and where data was tracked and processed accordingly, the two cotton rows were not considered different experimentally. Therefore the possible two crop row variability was not considered.

CropCircle raw data adjustment approaches -
Three undescribed adjustment value test calculation data columns are included, appended to the original raw data tables. For each CC sensor detector, the white panel observed amplitude delta of the raw reflectance channel was used to create minor data adjustments. This calculated test value was appended to the raw data table as variables R1_adj, R2_adj and R3_adj, and example a possible raw data minor adjustment.

This was the beginning period of a method advancement, in testing control based normalization adjustments to raw active optical detector data values. Generic and course post-process raw data adjustments can be made by first measuring a white panel reference at 120 cm distant, before and / or after a data collection period, which is beyond using only the SC-1 device to normalize individual sensor detectors. A deviation from the flat white reflectance typical 1.0 unity value was recorded and used to offset the detector raw radiance values.

The raw data adjustment test approach was developed as an extension of the manufacturer’s normalization routine recommendation, which uses the SC-1 device or a titanium dioxide ultra-white painted custom panel. Normally, the ACS-470 detector channels would be set to read 1.0, after 30 minutes of warmup time and when connected to the SC-1 illumination reflector, or when held 120 cm away from and facing an optically flat white panel of sufficient size to fully reflect the active light signal footprint (about 30 x 100 cm). This recommended approach does work well.

We normalized multiple sensors in a field condition, by using the typical two tailed white panel field-normalization approach. One by one, each sensor was connected to the SC-1 box for communications with a PC, where the sensor real-time information was viewed and a sensor normalization command given. Once placed at an appropriate height and position relative to the white panel, a sensor zero point as measured was ascribed to the sensor configuration, by first covering the active optical LEDs source and detectors and creating a black-out condition, and then immediately afterward revealing the illuminated white panel in full detector view where a second full signal measurement was made and the unity 1.0 set point value instructed to the sensor.

Values streaming through the active optical sensor detectors typical range 0-2% around the unity value after field normalization, while in a natural condition measuring the course surface white control panel. Therefore successful normalization was deemed to have occurred, or was not needed, when all detectors were within 2% of the 1.0 value when using the white panel setup. It was difficult to achieve a 1% data range for all the detectors at all times, where multiple iterations of the normalization routine would not consistently yield improved results of 1% magnitude.

Therefore, we simply measured the typical 0-2% raw data value difference for each detector, with the idea that a subsequent adjustment may be possible. We found that we could measure longer time periods with sensors over a white panel reference and determine optical signal features, as well as elucidate individual sensor minor behavior. Temperature change apparently induced effects on the raw detector data stream. We also recorded the sensors when connected optically to the SC-1 device reflector, in dark conditions, at various distances and angles from a target, and with many different types of target reflectors, in a temperature control room, laboratory, shop and outdoors.

We noted that each detector of each sensor can exhibit unique behaviors, which underlie the customizable band-pass color filter’s effect. Some detector channels

Overview The PCCF+ is a SAS control program and set of associated datasets derived from the PCCF, a 2021 postal code population weight file, the Geographic Attribute File, Health Region boundary files, and other supplementary data. PCCF+ automatically assigns a range of Statistics Canada standard geographic areas and other geographic identifiers based on postal codes. PCCF+ differs from the PCCF in that it: Uses population-weighted random allocation for many postal codes that link to more than one geographic area. Options are available for institutional postal codes. Procedures are included to link partial postal codes to geographic identifiers where possible. Problem records and diagnostics are provided in the program output, along with reference information for possible solutions. The geographic coordinates, which represent the standard geostatistical areas linked to each postal code on the PCCF, are commonly used to map the distribution of data for spatial analysis (e.g., clients, activities). In April 1983, the Geography Division released the first version of the PCCF, which linked postal codes to 1981 census geographic areas and included geographic coordinates. PCCF+ was first created using the 1986 census and has been updated regularly with population weight files calculated for each census from 1991 through 2021. Purpose of PCCF+ The purpose of the PCCF+ is to provide a link between six-character postal codes produced by the Canada Post Corporation (CPC), standard 2021 Census geographic areas (such as dissemination areas, census subdivisions, and census tracts) produced by Statistics Canada, and supplementary administrative areas and neighbourhood income quintiles. Postal codes do not respect census geographic boundaries and so may be linked to more than one standard geographic area, or assigned to more than one set of coordinates. Therefore, one postal code may be represented by more than one record. The PCCF product, produced by Statistics Canada, provides links between postal codes and all recorded matches to census geography. PCCF+ uses the PCCF but provides additional functionality in that it uses a population-weighted matching process for some residential postal codes where more than one geographic code is possible. PCCF+ also provides routines for institutional postal codes and for historic postal codes. The PCCF+ Version 8B includes a population-weighting file calculated from the 2021 Census population counts Neighbourhood income quintiles and deciles have been calculated from 2021 Census population data. The routine that allowed geocoding of historical postal codes in British Columbia (V1H, V9G, prior 1998) has been removed.

Tento datový soubor splňuje specifikace režimu „Část vozidel s nízkými emisemi při obnově vozového parku“, který je k dispozici na schema.data.gouv.fr.

This dataset summarizes land cover for each of 65 small watersheds along the Virginia portion of the Atlantic Coast of the Delmarva Peninsula. Watersheds were delineated by Bruce Hayden and John Porter from USGS 1:24,000 scale quadrangle maps and extend down to the 5 ft (1.524) contour. Land cover data comes from the NOAA C-CAP dataset from 1988. To develop this dataset, the watersheds were digitized as ARC/INFO coverages and overlayed with the CCAP data (converted to ARC/INFO polygons from its native ERDAS raster format). The resulting polygon areas were then tabulated using SAS to generate this data file.