Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Data Description: This data set contains all inspections issued/performed by City of Cincinnati Departments (including Buildings & Inspections; Cincinnati Fire Department; Cincinnati Health Department; Cincinnati Parks; and Trade/Development), as well as Inspections Bureau Inc (IBI) and Hamilton County departments.

Inspections range from electrical surveys, to swimming pools/spas, to elevator inspections, daycare inspections, and more. This data covers inspections since 1999 through present day.

Data Creation: All data is input by respective agencies, and maintained/stored by Cincinnati Area Geographic Information Systems (CAGIS), and is additionally available on CAGIS Property Activity Report website: http://cagismaps.hamilton-co.org/PropertyActivity/cagisreport

Data Created By: CAGIS

Refresh Frequency: Daily

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

Active non-wholesale non-transmission Natural Gas Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

iRAP Star Rating for Designs Services Market Outlook

As per the latest research, the global iRAP Star Rating for Designs Services market size in 2024 stands at USD 1.18 billion, reflecting a robust demand driven by increasing road safety initiatives worldwide. The market is expected to grow at a CAGR of 7.4% during the forecast period, reaching a projected value of USD 2.23 billion by 2033. This growth is primarily attributed to the rising emphasis on road infrastructure safety, regulatory mandates for safer road designs, and the adoption of internationally recognized assessment protocols, such as the iRAP Star Rating system, by governments and private entities. As per our comprehensive analysis, the market’s expansion is underpinned by technological advancements and a strong policy push for reducing road fatalities globally.

The growth trajectory of the iRAP Star Rating for Designs Services market is significantly influenced by the increasing global awareness regarding road safety and the critical need to minimize road traffic injuries and fatalities. Governments and transport authorities are actively pursuing strategies to align with the United Nations’ Decade of Action for Road Safety, which aims for a substantial reduction in road deaths by 2030. The iRAP Star Rating methodology, which objectively evaluates the safety of road designs, has become a cornerstone for such initiatives. This market’s expansion is further propelled by the growing number of public-private partnerships, regulatory mandates for safety audits, and the integration of advanced analytics and simulation tools into road design processes. As urbanization accelerates and vehicle populations surge, especially in emerging economies, the demand for robust safety assessment and design consultancy services is forecasted to remain strong.

Another major driver for market growth is the increasing complexity of road networks and the need for holistic, evidence-based design solutions. Urbanization and rapid infrastructure development have resulted in more intricate intersections, higher traffic volumes, and diverse road user profiles, all of which require sophisticated safety assessment tools. The iRAP Star Rating system offers a standardized approach to evaluating and improving road safety performance, making it an attractive solution for governments, engineering firms, and contractors. Moreover, the rising frequency of road safety audits, coupled with the availability of specialized training and certification programs, is fostering a skilled workforce capable of delivering high-quality design services. The growing integration of digital technologies, such as Geographic Information Systems (GIS) and Building Information Modeling (BIM), is also enhancing the accuracy and efficiency of safety assessments, further boosting market demand.

The market is also benefitting from increased funding and investment in road safety infrastructure, particularly from international development agencies and multilateral organizations. Financial support from entities such as the World Bank, Asian Development Bank, and regional governments is enabling the implementation of large-scale road safety projects that incorporate iRAP Star Rating assessments from the planning and design stages. This trend is particularly pronounced in low- and middle-income countries, where road traffic injury rates are highest and the need for safer infrastructure is most acute. Additionally, the emergence of smart city initiatives and the adoption of Vision Zero policies in various regions are creating new opportunities for the deployment of advanced road safety design services. The growing recognition of the economic and social costs associated with road traffic accidents is prompting stakeholders to prioritize safety-oriented design, thereby sustaining the market’s upward momentum.

Regionally, the adoption of iRAP Star Rating for Designs Services varies, with North America and Europe leading in terms of market share and technological advancement. Asia Pacific, however, is emerging as the fastest-growing region, driven by massive infrastructure investments and heightened policy focus on road safety. The Middle East & Africa and Latin America are also witnessing increased uptake, supported by international funding and regional safety campaigns. Each region presents unique challenges and opportunities, ranging from mature regulatory environments in developed markets to capacity-building needs in developing economies. The regional outlook for t

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center

Forestry Software Market size was valued at USD 1.29 Billion in 2024 and is projected to reach USD 7.95 Billion by 2031, growing at a CAGR of 22.39% during the forecasted period 2024 to 2031.

The Forestry Software Market is experiencing significant growth driven by several factors. Firstly, increasing global concerns regarding deforestation, environmental conservation, and sustainable forestry practices are compelling forestry organizations to adopt digital solutions for efficient management of resources. Secondly, technological advancements, such as Geographic Information System (GIS) integration, remote sensing, and cloud computing, are enhancing the capabilities of forestry software, enabling better decision-making processes and resource optimization. Thirdly, the rising demand for timber, coupled with the need for improved operational efficiency and cost reduction in forestry operations, is driving the adoption of software solutions for inventory management, harvest planning, and logistics optimization. Moreover, regulatory requirements for compliance with environmental standards and certification programs are further incentivizing the adoption of forestry software solutions. Additionally, the emergence of mobile-based applications and field data collection tools is facilitating real-time monitoring and data analysis, contributing to market growth.

Drone Cinematography Services Market Outlook

According to our latest research, the global drone cinematography services market size reached USD 3.8 billion in 2024, driven by rapidly expanding demand across diverse industries and a strong adoption curve for aerial imaging technologies. The market is projected to grow at a remarkable CAGR of 16.2% from 2025 to 2033, resulting in a forecasted market size of USD 15.6 billion by 2033. This robust growth trajectory is attributed to the increasing integration of drone-based solutions in media production, real estate, construction, and various commercial sectors, as well as the continual advancement in drone hardware and imaging capabilities.

The primary growth factor for the drone cinematography services market is the surging demand for high-definition aerial content in the media and entertainment sector. As cinematic storytelling evolves, production houses, streaming platforms, and advertising agencies are leveraging drones to capture breathtaking, cost-effective, and unique perspectives that were previously unattainable or prohibitively expensive. The proliferation of 4K and even 8K camera-equipped drones, combined with sophisticated stabilizing technologies, has revolutionized the way visual narratives are created, making drone cinematography a staple in modern filmmaking, documentaries, and live event broadcasting. Furthermore, the ability to capture dynamic shots in challenging environments without putting human operators at risk has solidified drones as an indispensable tool in visual content creation.

Another significant driver is the widespread adoption of drone cinematography in the real estate and construction industries. Real estate professionals are increasingly utilizing aerial photography and videography to showcase properties, developments, and landscapes in a visually compelling manner, enhancing marketing efforts and accelerating sales cycles. In construction, drones are employed for progress monitoring, site surveying, and mapping, providing stakeholders with real-time, high-resolution visuals that improve project management and decision-making. The integration of drone data with Building Information Modeling (BIM) and Geographic Information Systems (GIS) further amplifies the value proposition, enabling precise planning, risk assessment, and resource allocation.

Technological advancements in drone platforms and imaging systems are also fueling market expansion. The evolution of multi-rotor and hybrid drones, featuring extended flight times, enhanced payload capacities, and AI-driven automation, has broadened the scope of drone cinematography services. These innovations have reduced operational barriers, increased efficiency, and allowed service providers to cater to a wider range of applications, from live event coverage to large-scale infrastructure inspections. Additionally, regulatory relaxation in several regions, coupled with the introduction of standardized pilot training and certification programs, has facilitated market entry and fostered a competitive ecosystem ripe for innovation.

Regionally, North America remains the dominant force in the drone cinematography services market, owing to early technology adoption, robust regulatory frameworks, and significant investments in media production and real estate. The Asia Pacific region, however, is emerging as the fastest-growing market, propelled by rapid urbanization, increasing digitalization, and a burgeoning entertainment industry. Europe continues to demonstrate steady growth, driven by the integration of drones in tourism, advertising, and government projects. Latin America and the Middle East & Africa are gradually catching up, with expanding commercial applications and supportive government initiatives fueling market penetration.

Service Type Analysis

The drone cinematography services market is segmented by service type into aerial filming, event coverage, real estate photography, surveying & mapping, and others. Aerial filming

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

A Certificate of Convenience and Necessity (CCN) is issued by the PUCT, and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies.This dataset is a Texas statewide polyline layer of water CCN facility lines. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced.This dataset is a Texas statewide polyline layer of water Certificates of Convenience and Necessity (CCN) facility lines. This type of CCN may either be a Facilities Only (F0), a CCN Facility line (point of use) service area that covers only the customer connections at the time the CCN was granted, or Facilities plus a specified number of feet (usually 200 feet buffer) around the facility line. It is best to view the water CCN facility lines in conjunction with the water CCN service areas, since these two layers together represent all of the retail public water utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: October 4, 2022The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.8.2.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - Indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - A unique five-digit number assigned to each CCN when it is created and approved by the Commission. *CCN number starting with an ‘N’ indicates an exempt utility.UTILITY - The name of the utility which owns the CCN.COUNTY - The name(s) of the county(ies) in which the CCN exist.CCN_TYPE –One of three types:Bounded Service Area: A certificated service area with closed boundaries that often follow identifiable physical and cultural features such as roads, rivers, streams and political boundaries. Facilities +200 Feet: A certificated service area represented by lines. They include a buffer of a specified number of feet (usually 200 feet). The lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.Facilities Only: A certificated service area represented by lines. They are granted for a "point of use" that covers only the customer connections at the time the CCN is granted. Facility only service lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.STATUS – For pending dockets check the PUC Interchange Filing Search

Alaska school district boundaries and addresses. Replacing former School District geometries (as of Nov 3, 2022). Each school district boundary (geometry, not attributes) is derived from one of three sources. 1. Borough and Census Area Boundaries (as a proxy for school districts in the organized borough). https://dcra-cdo-dcced.opendata.arcgis.com/datasets/DCCED::alaska-borough-and-census-area-boundaries/about2. Regional Educational Attendance Area Boundaries (school districts in the unorganized borough). A Regional Educational Attendance Area (REAA) is an educational area that is established in an unorganized borough of the state established by AS 14.08.031(a). REAA elections administered by the Division of Elections. This dataset is revised for recasting of REAA sections using 2020 Census data. The 2020 version is the first time REAA boundaries were fully created using Geographic Information System (GIS) technology. Revised for the 2022 move of the city of Rampart from Yukon Flats to Yukon-Koyukuk. 3. City Boundaries - Boundaries are based on the actual certificates issued by the Local Boundary Commission. https://dcra-cdo-dcced.opendata.arcgis.com/datasets/DCCED::city-boundaries/aboutFor more information, see https://education.alaska.gov/DOE_Rolodex/SchoolCalendar/DistrictAndSchoolInfo/DistrictDetails

A Certificate of Convenience and Necessity (CCN) is issued by the Public Utility Commission of Texas (PUCT), and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies. This dataset is a Texas statewide polygon layer of sewer CCN service areas. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced. It is best to view the sewer CCN service area data in conjunction with the sewer CCN facility line data, since these two layers together represent all of the retail public sewer utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: January 29, 2024The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.8.2.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - Indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - A unique five-digit number assigned to each CCN when it is created and approved by the Commission. *CCN number starting with an ‘N’ indicates an exempt utility.UTILITY - The name of the utility which owns the CCN.COUNTY - The name(s) of the county(ies) in which the CCN exist.CCN_TYPE –One of three types:Bounded Service Area: A certificated service area with closed boundaries that often follow identifiable physical and cultural features such as roads, rivers, streams and political boundaries. Facilities +200 Feet: A certificated service area represented by lines. They include a buffer of a specified number of feet (usually 200 feet). The lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.Facilities Only: A certificated service area represented by lines. They are granted for a "point of use" that covers only the customer connections at the time the CCN is granted. Facility only service lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.STATUS – For pending dockets check the PUC Interchange Filing Search

This data set is a mosiac of 3.75-minute Latitude by 3.75-minute Longitude black and white 1 meter pixel resolution digital orthophoto quads of Fayette County, Ohio taken in 1994 for the USDA. The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.USDA Metadata:Identification_Information:Citation:Citation_Information:Originator:U.S. Department of Agriculture, Natural Resources Conservation ServicePublication_Date: 20020627Title: Orthophoto Mosaic for Fayette County, OHGeospatial_Data_Presentation_Form: remote-sensing imageOnline_Linkage: \NCGC0004\f\data\doq\geodata\ortho_imagery\oh_fayette_mos.sidDescription:Abstract:Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquad (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75-minute of latitude and 3.75-minute of longitude) image cast on the Universal Transverse Mercator Projection (UTM) on the North American Datum of 1983 (NAD83). The normal orientation data is by lines (rows) and samples (columns). Each contains a series of pixels ordered from west to with the order of the lines from north to south. The radiometric image values are stored as 256 gray levels ranging 0 to 255.Purpose:Digital orthophotos serve a variety of purposes, from interim maps to references for earth science investigations and. The images are useful as a layer of a geographic information system and as a tool for revision of digital graphs and topographic maps.These data are prepared for use by the Natural Resources Conservation Service for USDA Service Center personnel to administer agency programs.Time_Period_of_Content:Time_Period_Information:Range_of_Dates/Times:Beginning_Date: 19940317Ending_Date: 19940424Currentness_Reference: ground conditionStatus:Progress: CompleteMaintenance_and_Update_Frequency: As neededSpatial_Domain:Bounding_Coordinates:West_Bounding_Coordinate: -83.763583East_Bounding_Coordinate: -83.106265North_Bounding_Coordinate: 39.766002South_Bounding_Coordinate: 39.236672Keywords:Theme:Theme_Keyword_Thesaurus: NoneTheme_Keyword: digital orthophotoTheme_Keyword: digital image mapTheme_Keyword: aerial photographTheme_Keyword: rectified photographTheme_Keyword: rectified imageTheme_Keyword: orthophotoTheme_Keyword: DOQTheme_Keyword: DOQQPlace:Place_Keyword_Thesaurus:Counties and County Equivalents of the States of the United States and the District of Columbia (FIPS Pub 6-3)Place_Keyword: Fayette CountyPlace_Keyword: OhioPlace_Keyword: United StatesAccess_Constraints: None.Use_Constraints:These data were prepared for Official Use Only by USDA employees as part of the Service Center Initiative.Point_of_Contact:Contact_Information:Contact_Organization_Primary:Contact_Organization:U.S. Department of Agriculture, Natural Resources Conservation ServiceContact_Person: Geospatial Data BranchContact_Address:Address_Type: mailing and physical addressAddress: Federal Center, 501 W. Felix St., Bldg 23, P.O Box 6567City: Fort WorthState_or_Province: TexasPostal_Code: 76115Country: USAContact_Voice_Telephone: (817) 509-3400Contact_Facsimile_Telephone: (817) 509-3469Hours_of_Service: 8:00 am to 4:30 pm, CentralBrowse_Graphic:Browse_Graphic_File_Name: unavailableBrowse_Graphic_File_Description: unavailableBrowse_Graphic_File_Type: unavailableNative_Data_Set_Environment:Microsoft Windows NT Version 4.0 (Build 1381) Service Pack 6; ESRI ArcCatalog 8.1.1.649Data_Quality_Information:Attribute_Accuracy:Attribute_Accuracy_Report:Image brightness values may deviate from brightness values of original imagery due to image value interpolation during scanning and rectification processes. Radiometry is verified by USGS through a visual inspection of the digital quadrangle with the original unrectified image to determine if the digital orthophoto has the same or better quality as the original unrectified input image. Radiometric differences can be detected adjacent DOQ files due primarily to differences in photography capture dates and sun angles of aerial along flight lines. These differences can be in an image's general lightness or darkness when compared to adjacent DOQ file coverages.Logical_Consistency_Report: Not ApplicableCompleteness_Report:All DOQ image mosaics are visually inspected for completeness to ensure the area of interest is included. Original images are almost entirely cloud free. Source photography is leaf-off in deciduous vegetation regions. Void areas having a radiometric value of zero and appearing black may exist.Positional_Accuracy:Horizontal_Positional_Accuracy:Horizontal_Positional_Accuracy_Report:The horizontal positional accuracy and the assurance of that accuracy depend, in part, on the accuracy of the data inputs to the rectification process. These inputs consist of the digital elevation model (DEM),aerotriangulation control and methods, the photo source camera calibration, scanner calibration, and aerial photographs that meet National Aerial Photography Program (NAPP) standards. The vertical accuracy of the verified USGS format DEM is equivalent to or better than a USGS level 1 or 2 DEM, with a root mean square error (RMSE) of no greater than 7.0 meters. Field control is acquired by third order class 1 or better survey methods sufficiently spaced to meet National Map Accuracy Staandards (NMAS) for 1:12,000-scale products. Aerial cameras have current certification from the USGS, National Mapping Division, Optical Science Laboratory. Test calibration scans are performed on all source photography scanners. Horizontal positional accuracy is determined by the Orthophoto Accuracy (ORACC) software program for DOQ data produced by the National Mapping Division. The program determines the accuracy by finding the line and sample coordinates of the passpoints in the DOQ and fitting these to their ground coordinates to develop a root mean square error (RMSE). From 4 to 9 points are checked. As a further accuracy test, the image line and sample coordinates of the DEM corners are transformed and compared with the actual X,Y DEM corner values to determine if they are within the RMSE. Additional information on this testing procedure can be found in U.S. Department of the Interior, U.S. Geological Survey, 1993, Technical Instructions, ORACC Users Manual (draft): Reston, VA. Adjacent DOQ's, when displayed together in a common planimetric coordinate system, may exhibit slight positional discrepancies across common DOQ boundaries. Linear features, such as streets, may not be continuous. Field investigations to validate DOQ positional accuracy reliabilty are periodically conducted by the USGS, National Mapping Division, Geometronics Standards Section. DOQ's produced by cooperators and contractors use similarly approved RMSE test procedures.Quantitative_Horizontal_Positional_Accuracy_Assessment:Horizontal_Positional_Accuracy_Value: 7 metersHorizontal_Positional_Accuracy_Explanation:U.S.Bureau of the Budget, 1947, United States National Map Accuracy Standard.Vertical_Positional_Accuracy:Vertical_Positional_Accuracy_Report: NALineage:Source_Information:Source_Citation:Citation_Information:Originator: U.S. Geological SurveyPublication_Date: UnknownPublication_Time: UnknownTitle: DOQQGeospatial_Data_Presentation_Form: remote-sensing imagePublication_Information:Publication_Place: Reston, VAPublisher: U.S. Geological SurveySource_Scale_Denominator: 40,000Type_of_Source_Media: CD-ROMSource_Time_Period_of_Content:Time_Period_Information:Range_of_Dates/Times:Beginning_Date: 19940317Ending_Date: 19940424Source_Currentness_Reference: ground conditionSource_Citation_Abbreviation: DOQQSource_Contribution:Panchromatic black and white (or color infra-red) NAPP or NAPP-like photographs. NAPP photographs are centered on the DOQ coverage area, the primary images making up the county mosaics.Process_Step:Process_Description:The production procedures, instrumentation, hardware and software used in the collection of standard USGS DOQ's vary depending on systems used at the contract, cooperator or USGS production sites. The majority of DOQ datasets are acquired through government contract. The process step describes, in general, the process used in the production of USGS DOQ data sets. The rectification process requires, as input, a user parameter file to the rectification process, a digital elevation model gridded to specified bounds, projection, zone, datum and X-Y units, a scanned image file covering the same area as the DEM, ground X-Y-Z point values and their conjugate photo coordinates in the camera coordinate system, and measurements of the fiducial in the digitized image.The camera calibration report provides the length of the camera and the distances in millimeters from the optical center to the camera's 8 fiducial marks. These marks define the field of reference for spatial measurements made from the photograph. Ground control points acquired from ground surveys or developed in aerotriangulation, are third order class 1 or better, and meet National Map Accuracy Standard (NMAS) for 1:12,000-scale. Ground control points are in the Universal Transverse Mercator or the State Plane Coordinate System on NAD83. Horizontal and vertical residuals of aerotriangulated tie-points are equal to or less than 2.5 meters. Standard aerotriangulation passpoint configuration consists of 9 ground control points, one near each corner, one at the center near each side and 1 near the center of the photograph, are used. The conjugate positions of the ground control points on the photograph are measured and recorded in camera coordinates.The raster image file is created by scanning an aerial photograph film diapositive with a precision image scanner. An aperture of approximately 25 to 32 microns is used, with an aperture no greater than 32 microns permitted. Using 1:40,000-scale photographs, a 25-micron scan aperture equates to a ground resolution of