The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences. Geo-Analytical Factors Each store in your database is further enhanced with geo-analytical data. We analyze: Maximum pedestrian and vehicle traffic within a defined radius Number of households and average income within the catchment area vicinity Number of schools, hospitals, universities, competitors, stores, bars, clubs, and restaurants in the surrounding area Point attendance based on mobile device location data (ensuring GDPR compliance) Our refined retail data collection and analysis provides detailed shopping behavior insights, leading to in-depth shopper analytics and retail foot traffic data that support strategic planning and execution. The Power of Points of Interest (POI) Data At BestPlace, we harness the power of Point of Interest (POI) data (to bring you the most complete retail data set.) to bring your retail data to life. Our POI data collection process involves analyzing and categorizing foot traffic data, providing a comprehensive foot traffic dataset as a result. This data allows you to understand the ebb and flow of individuals around your store locations, suggesting invaluable insights for strategic planning and operational efficiency. Leveraging GIS Data Our GIS data collection process is meticulous and comprehensive. We tap into multiple GIS data sources, providing a wealth of data to enhance your retail analytics. This process allows us to equip your database with a broad range of geospatial features, including demographic and socioeconomic information from various census data for GIS applications. By including GIS data in your analysis, you gain a multi-dimensional perspective of your retail landscape, allowing for more strategic decision-making. The Advantages of Census Data BestPlace grants you direct access to a wealth of census data sets. This transforms your retail database into a more potent tool for decision-making, providing a deeper understanding of the demographics and socioeconomic factors surrounding your store locations. With the ability to download census data directly, you can enrich your retail data analysis with valuable insights about potential customers, giving you the upper hand in your strategic planning. Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics. Location Comparison: Compare multiple store locations based on numerous factors and make informed decisions about where to focus your resources. Distribution Optimization: Leverage our FMCG data analytics and retail traffic analytics to optimize your distribution strategy and maximize ROI. Building Machine Learning Models: Integrate our all-purpose machine learning models into your business decision processes to enable more efficient and effective decision-making. (Integrate our all-purpose machine learning models to build your own in-house solutions with the help of our data.) Comprehensive Deliverables As a BestPlace client, you receive a comprehensive produc...

The National Flood Hazard Layer (NFHL) data incorporates all Flood Insurance Rate Map (FIRM) databases published by the Federal Emergency Management Agency (FEMA), and any Letters of Map Revision (LOMRs) that have been issued against those databases since their publication date. It is updated on a monthly basis. The FIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper FIRMs. The FIRM Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by FEMA. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all FIRM Databases and corresponding LOMRs available on the publication date of the data set. The specification for the horizontal control of FIRM Databases is consistent with those required for mapping at a scale of 1:12,000. This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.This dataset published by FEMA on 5/18/2016. Available for download at https://msc.fema.gov.This GIS data is provided "AS IS." The County of Monterey (COUNTY) makes no warranties, express or implied, including without limitation, any implied warranties of merchantability and/or fitness for a particular purpose, regarding the accuracy, completeness, value, quality, validity, merchantability, suitability, and/or condition, of the GIS data. The COUNTY also specifically does not guarantee that the information is free from harmful effects or viruses and that it will not harm the users’ computer. By using this GIS, users accept sole responsibility for ensuring the protection of their own computer equipment and specifically hold COUNTY harmless from any damage or liability that might ensue do the use of the data. Users of COUNTY's GIS data are hereby notified that current public primary information sources should be consulted for verification of the data and information contained herein. Since the GIS data is dynamic, it will by its nature be inconsistent with the official COUNTY assessment roll file, surveys, maps and/or other documents produced by the County Office of the Assessor, the County Surveyor, and/or other relevant County Offices. Any use of COUNTY's GIS data is done exclusively at the risk of the party making such use.

We started with ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, we reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a few more examples that meet or come very close to the full-service grocery store criteria. Here’s the explanation from OP regarding how they came to create their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”We also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

This StoryMap is designed to help teachers guide students through applying spatial analysis to prepare a presentation for a New Zealand company that is looking to create a new ski field specialising in advanced skiing terrain in the South Island High Country Mackenzie District Council area. People have requested facilities which are suitable for advanced skiers only and so the company has contracted you to locate three possible sites for the new ski field and then make a recommendation on the best site.

The ski field’s location has to meet the following requirements for it to be effective:

on land that is between 1300 and 1800 metres in elevationwithin 10 km of a major road, for accessibility

between 40 and 60 degrees slope, which is identified as being ideal slopes for advanced skiers

Students have their own assessment materials to work through, you should not give them access to this Story Map. Click the link below to open the student assessment materials.Student Materials

All property addresses for City of Kitchener, which includes business names.All addresses are provided and only addresses not covered under MFIPPA are shown. MFIPPA is Ontario Municipal Freedom of Information and Protection of Privacy Act - The Act requires that local government institutions protect the privacy of an individual's personal information existing in government records. Only the owner of a business (or number company) or government agency can be shown, and all other addresses are marked as private.

Overview   This dataset provides the measurements of raw water storage levels in reservoirs crucial for public water supply, The reservoirs included in this dataset are natural bodies of water that have been dammed to store untreated water.    Key Definitions   Aggregation  The process of summarizing or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.    Capacity The maximum volume of water a reservoir can hold above the natural level of the surrounding land, with thresholds for regulation at 10,000 cubic meters in England, Wales and Northern Ireland and a modified threshold of 25,000 cubic meters in Scotland pending full implementation of the Reservoirs (Scotland) Act 2011. Current Level The present volume of water held in a reservoir measured above a set baseline crucial for safety and regulatory compliance. Current Percentage The current water volume in a reservoir as a percentage of its total capacity, indicating how full the reservoir is at any given time. Dataset  Structured and organized collection of related elements, often stored digitally, used for analysis and interpretation in various fields.   Granularity  Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours  ID  Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.   Open Data Triage  The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.   Reservoir Large natural lake used for storing raw water intended for human consumption. Its volume is measurable, allowing for careful management and monitoring to meet demand for clean, safe water. Reservoir Type The classification of a reservoir based on the method of construction, the purpose it serves or the source of water it stores. Schema  Structure for organizing and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.   Units  Standard measurements used to quantify and compare different physical quantities.     Data History   Data Origin   Reservoir level data is sourced from water companies who may also update this information on their website and government publications such as the Water situation reports provided by the UK government. Data Triage Considerations  Identification of Critical Infrastructure Special attention is given to safeguard data on essential reservoirs in line with the National Infrastructure Act, to mitigate security risks and ensure resilience of public water systems. Currently, it is agreed that only reservoirs with a location already available in the public domain are included in this dataset. Commercial Risks and Anonymisation The risk of personal information exposure is minimal to none since the data concerns reservoir levels, which are not linked to individuals or households. Data Freshness It is not currently possible to make the dataset live. Some companies have digital monitoring, and some are measuring reservoir levels analogically. This dataset may not be used to determine reservoir level in place of visual checks where these are advised. Data Triage Review Frequency   Annually unless otherwise requested  Data Specifications  Data specifications define what is included and excluded in the dataset to maintain clarity and focus. For this dataset: Each dataset covers measurements taken by the publisher. This dataset is published periodically in line with the publisher’s capabilities Historical datasets may be provided for comparison but are not required The location data provided may be a point from anywhere within the body of water or on its boundary. Reservoirs included in the dataset must be: Open bodies of water used to store raw/untreated water Filled naturally Measurable Contain water that may go on to be used for public supply Context  This dataset must not be used to determine the implementation of low supply or high supply measures such as hose pipe bans being put in place or removed. Please await guidance from your water supplier regarding any changes required to your usage of water. Particularly high or low reservoir levels may be considered normal or as expected given the season or recent weather. This dataset does not remove the requirement for visual checks on reservoir level that are in place for caving/pot holing safety. Some water companies calculate the capacity of reservoirs differently than others. The capacity can mean the useable volume of the reservoir or the overall volume that can be held in the reservoir including water below the water table. Data Publish Frequency   Annually

This is a feature point layer of the 124 licensed personal care homes (PCHs) in Manitoba. All licensed PCHs in Manitoba are required to comply with minimum standards of care as set out in the Personal Care Home Standards Regulation under the Health Services Insurance Act. The Licensing and Compliance Branch of Manitoba Health Seniors and Active Living monitors compliance through regular review processes. PCH operators are required to take the necessary steps to address concerns identified in the course of reviews within specified time lines and must provide status updates until concerns have been addressed. PCH licences are reviewed and renewed annually and review findings are used to inform decision-making. The dataset includes the following fields (Alias (Name): Description) Regional Health Authority (Regional_Health_Authority): The name of the Regional Health Authority in which the facility is located. Community (Community): The name of the community in which the facility is located. Facility (Facility): The name of the licensed personal care home. Facility Key (Facility_Key): Primary key used to query records in the Summary Reviews table. Facility Label (Facility_Label): An abbreviated facility name suitable for use as a label in a map. Address (Address): The street address of the facility. Postal Code (Postal_Code): The postal code for the facility. Phone Number (Phone_Number): The phone number for the facility. Proprietary Status (Proprietary_Status): Refers to the ownership of the facility, either Proprietary or Non-proprietary. Language (Language): The designated language of the facility, either English or Bilingual. Bed (Beds): The number of beds in the facility. Status of Licence (Status_of_Licence): The status of the facility’s license. Possible values are Unencumbered, Under Review, or With Conditions. Owner/Operator (Owner_Operator): The individual or company that owns the facility. Website (Website): The URL for the website of the facility. Latitude (Latitude): The latitudinal coordinate in decimal degrees. Longitude (Longitude): The longitudinal coordinate in decimal degrees.This feature point layer forms part of the data for the Manitoba Personal Care Home Reporting app.

Contours for the southern third of Stafford County. See map below to see coverage area.Contour MetadataIdentification_Information: Citation: Citation_Information: Originator: Virginia Geographic Information Network (VGIN) Publication_Date: 200702 Title: 2 and 4 Foot Contours (VA State Plane North) Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://www.vgin.virginia.gov Description: Abstract: The 2 and 4 foot contours were produced as part of the 2006 and 2007 orthophotography update cycle of the Virginia Geographic Information Network's (VGIN) Virginia Base Mapping Program (VBMP). Contours were provided to jurisdictions who chose to use the upgrade option for contour generation. The contours are provided in Personal Geodatabase format. Due to size limitations, a jurisdiction may contain more than one geodatabase. Purpose: The aerial photography and creation of the subsequent contour data was executed to capture the existing ground conditions at a specific point in time for the purpose of GIS analysis. VGIN sub-contracted with the Sanborn Map Company to execute a Statewide mapping contract in the years 2006 and 2007. This contract updated and enhanced the existing 2002 map data. The Virginia Geographic Information Network (VGIN) is the lead public agency in the Commonwealth for spatial data and GIS. VGIN's mission is to facilitate the cost-effective development and use of spatial data, GIS, and related technologies in organizations throughout the Commonwealth. Supplemental_Information: The following localities chose to upgrade to contours with the flights taking place in 2006: Alexandria, Culpeper, Fredericksburg, Harrisonburg, King George, Loudoun (Purcellville Town), Manassas, Manassas Park, Orange, Prince William, Stafford, and Waynesboro. The following localities chose to upgrade to contours with the flights taking place in 2007: Augusta, Clarke, Fairfax City, Orange, Page, Spotsylvania, and Warren. Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Currentness_Reference: publication date Status: Progress: Complete Maintenance_and_Update_Frequency: Unknown Spatial_Domain: Bounding_Coordinates: West_Bounding_Coordinate: -83.837830 East_Bounding_Coordinate: -75.160162 North_Bounding_Coordinate: 39.471482 South_Bounding_Coordinate: 36.464993 Keywords: Theme: Theme_Keyword_Thesaurus: None Theme_Keyword: contours Place: Place_Keyword_Thesaurus: None Place_Keyword: Alexandria Place_Keyword: Culpeper Place_Keyword: Fredericksburg Place_Keyword: Harrisonburg Place_Keyword: King George Place_Keyword: Loudoun Place_Keyword: Purcellville Place_Keyword: Manassas Place_Keyword: Manassas Park Place_Keyword: Orange Place_Keyword: Prince William Place_Keyword: Stafford Place_Keyword: Waynesboro Place_Keyword: Augusta Place_Keyword: Clarke Place_Keyword: Fairfax City Place_Keyword: Orange Place_Keyword: Page Place_Keyword: Spotsylvania Place_Keyword: Warren Access_Constraints: Contact VGIN for information about regional and statewide VBMP datasets. For information about local datasets, contact the appropriate locality. Use_Constraints: Any person not licensed as a land surveyor preparing documentation pursuant to subsection C of 54.1-402 of the Code of Virginia shall note the following on such documentation: "Any determination of topography or contours, or any depiction of physical improvements, property lines or boundaries is for general information only and shall not be used for the design, modification, or construction of improvements to real property or for flood plain determination." This VBMP data has been developed using procedures designed to produce data to National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 100' or 1" = 200' scale. It is requested that the Commonwealth of Virginia be cited for any use of the orthophotography as follows: "Imagery Courtesy of the Commonwealth of Virginia". Point_of_Contact: Contact_Information: Contact_Person_Primary: Contact_Person: Stuart Blankenship Contact_Organization: Virginia Geographic Information Network (VGIN) Contact_Position: Geospatial Project Manager Contact_Address: Address_Type: mailing and physical address Address: 11751 Meadowville Lane City: Chester State_or_Province: VA Postal_Code: 23836 Country: USA Contact_Voice_Telephone: (804) 416-6208 Contact_Facsimile_Telephone: (804) 416-6353 Contact_Electronic_Mail_Address: stuart.blankenship@vita.virginia.gov Hours_of_Service: 8:00 AM - 5:00 PM Native_Data_Set_Environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.2.1.1332Data_Quality_Information: Logical_Consistency_Report: Breaklines captured along water features are topologically structured and hydrologically corrected for positive high-to-low stream flow. All hydrological features such as streams, ponds, lakes, river banks, dams, and associated structures are also topologically corrected and structured. Completeness_Report: The initial base topo DTM was further densified for the creation of contours that will meet both NSSDA vertical accuracy specifications and FEMA contours specifications. Lineage: Source_Information: Source_Citation: Citation_Information: Originator: VGIN Publication_Date: 200702 Title: 100 Scale Digital Terrain Models Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://gisdata.virginia.gov Source_Scale_Denominator: 1200 Type_of_Source_Media: disc Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Source_Currentness_Reference: publication date Source_Citation_Abbreviation: VBMP 100 Scale DTM Source_Contribution: Digital terrain models Source_Information: Source_Citation: Citation_Information: Originator: VGIN Publication_Date: 200702 Title: 200 Scale Digital Terrain Models Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://gisdata.virginia.gov Source_Scale_Denominator: 2400 Type_of_Source_Media: disc Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Source_Currentness_Reference: publication date Source_Citation_Abbreviation: VBMP 200 Scale DTM Source_Contribution: Digital Terrain Models Process_Step: Process_Description: The analog aerial photography that was captured during the Spring of 2006/2007 was collected at 1"=600' scale for 2 foot contours and 1"=1200' for 4 foot contours. The aerial photography was controlled with strategic photo-identifiable ground control points. The ground control points were referenced to a Statewide adjusted Network in North American Datum 1983; Virginia State Plane North. The film was scanned at 14 microns and the aerotriangulation was executed using Zeiss ISAT software. The accuracy of the aerotriangulation data supports 1"=100' mapping and the generation of 2 foot contours or 1"=200' mapping and the generation of 4 foot contours. The Digital Terrain Model (DTM) and subsequent Triangular Irregular Network (TIN) used to the interpolate the contours was photogrammetrically stereo-compiled using fully manual methods to collect masspoints and breaklines. The initial base topo DTM was further densified for the creation of contours that will meet both NSSDA vertical accuracy specifications and FEMA contours specifications. The data was initially compiled in tile (2,500' X 2,500' or 5,000' X 5,000') format and seamlessly merged together into GeoDatabase format for final delivery. Source_Used_Citation_Abbreviation: VBMP 100 Scale DTM Source_Used_Citation_Abbreviation: VBMP 200 Scale DTM Process_Date: 200702Spatial_Data_Organization_Information: Direct_Spatial_Reference_Method: Vector Point_and_Vector_Object_Information: SDTS_Terms_Description: SDTS_Point_and_Vector_Object_Type: String Point_and_Vector_Object_Count: 46020Spatial_Reference_Information: Horizontal_Coordinate_System_Definition: Planar: Grid_Coordinate_System: Grid_Coordinate_System_Name: State Plane Coordinate System State_Plane_Coordinate_System: SPCS_Zone_Identifier: 4501 Lambert_Conformal_Conic: Standard_Parallel: 38.033333 Standard_Parallel: 39.200000 Longitude_of_Central_Meridian: -78.500000 Latitude_of_Projection_Origin: 37.666667 False_Easting: 11482916.666667 False_Northing: 6561666.666667 Planar_Coordinate_Information: Planar_Coordinate_Encoding_Method: coordinate pair Coordinate_Representation: Abscissa_Resolution: 1 Ordinate_Resolution: 1 Planar_Distance_Units: survey feet Geodetic_Model: Horizontal_Datum_Name: North American Datum of 1983 Ellipsoid_Name: Geodetic Reference System 80 Semi-major_Axis: 6378137.000000 Denominator_of_Flattening_Ratio: 298.257222 Vertical_Coordinate_System_Definition: Altitude_System_Definition: Altitude_Datum_Name: North American Vertical Datum of 1988 Altitude_Resolution: 0.000100 Altitude_Distance_Units: feet Altitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinatesEntity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: 2 and 4 Foot Contours

Contours for the middle third of the county. The areas covered are depicted in the following map.Contour Metadata:Identification_Information: Citation: Citation_Information: Originator: Virginia Geographic Information Network (VGIN) Publication_Date: 200702 Title: 2 and 4 Foot Contours (VA State Plane North) Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://www.vgin.virginia.gov Description: Abstract: The 2 and 4 foot contours were produced as part of the 2006 and 2007 orthophotography update cycle of the Virginia Geographic Information Network's (VGIN) Virginia Base Mapping Program (VBMP). Contours were provided to jurisdictions who chose to use the upgrade option for contour generation. The contours are provided in Personal Geodatabase format. Due to size limitations, a jurisdiction may contain more than one geodatabase. Purpose: The aerial photography and creation of the subsequent contour data was executed to capture the existing ground conditions at a specific point in time for the purpose of GIS analysis. VGIN sub-contracted with the Sanborn Map Company to execute a Statewide mapping contract in the years 2006 and 2007. This contract updated and enhanced the existing 2002 map data. The Virginia Geographic Information Network (VGIN) is the lead public agency in the Commonwealth for spatial data and GIS. VGIN's mission is to facilitate the cost-effective development and use of spatial data, GIS, and related technologies in organizations throughout the Commonwealth. Supplemental_Information: The following localities chose to upgrade to contours with the flights taking place in 2006: Alexandria, Culpeper, Fredericksburg, Harrisonburg, King George, Loudoun (Purcellville Town), Manassas, Manassas Park, Orange, Prince William, Stafford, and Waynesboro. The following localities chose to upgrade to contours with the flights taking place in 2007: Augusta, Clarke, Fairfax City, Orange, Page, Spotsylvania, and Warren. Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Currentness_Reference: publication date Status: Progress: Complete Maintenance_and_Update_Frequency: Unknown Spatial_Domain: Bounding_Coordinates: West_Bounding_Coordinate: -83.837830 East_Bounding_Coordinate: -75.160162 North_Bounding_Coordinate: 39.471482 South_Bounding_Coordinate: 36.464993 Keywords: Theme: Theme_Keyword_Thesaurus: None Theme_Keyword: contours Place: Place_Keyword_Thesaurus: None Place_Keyword: Alexandria Place_Keyword: Culpeper Place_Keyword: Fredericksburg Place_Keyword: Harrisonburg Place_Keyword: King George Place_Keyword: Loudoun Place_Keyword: Purcellville Place_Keyword: Manassas Place_Keyword: Manassas Park Place_Keyword: Orange Place_Keyword: Prince William Place_Keyword: Stafford Place_Keyword: Waynesboro Place_Keyword: Augusta Place_Keyword: Clarke Place_Keyword: Fairfax City Place_Keyword: Orange Place_Keyword: Page Place_Keyword: Spotsylvania Place_Keyword: Warren Access_Constraints: Contact VGIN for information about regional and statewide VBMP datasets. For information about local datasets, contact the appropriate locality. Use_Constraints: Any person not licensed as a land surveyor preparing documentation pursuant to subsection C of 54.1-402 of the Code of Virginia shall note the following on such documentation: "Any determination of topography or contours, or any depiction of physical improvements, property lines or boundaries is for general information only and shall not be used for the design, modification, or construction of improvements to real property or for flood plain determination." This VBMP data has been developed using procedures designed to produce data to National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 100' or 1" = 200' scale. It is requested that the Commonwealth of Virginia be cited for any use of the orthophotography as follows: "Imagery Courtesy of the Commonwealth of Virginia". Point_of_Contact: Contact_Information: Contact_Person_Primary: Contact_Person: Stuart Blankenship Contact_Organization: Virginia Geographic Information Network (VGIN) Contact_Position: Geospatial Project Manager Contact_Address: Address_Type: mailing and physical address Address: 11751 Meadowville Lane City: Chester State_or_Province: VA Postal_Code: 23836 Country: USA Contact_Voice_Telephone: (804) 416-6208 Contact_Facsimile_Telephone: (804) 416-6353 Contact_Electronic_Mail_Address: stuart.blankenship@vita.virginia.gov Hours_of_Service: 8:00 AM - 5:00 PM Native_Data_Set_Environment: Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.2.1.1332Data_Quality_Information: Logical_Consistency_Report: Breaklines captured along water features are topologically structured and hydrologically corrected for positive high-to-low stream flow. All hydrological features such as streams, ponds, lakes, river banks, dams, and associated structures are also topologically corrected and structured. Completeness_Report: The initial base topo DTM was further densified for the creation of contours that will meet both NSSDA vertical accuracy specifications and FEMA contours specifications. Lineage: Source_Information: Source_Citation: Citation_Information: Originator: VGIN Publication_Date: 200702 Title: 100 Scale Digital Terrain Models Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://gisdata.virginia.gov Source_Scale_Denominator: 1200 Type_of_Source_Media: disc Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Source_Currentness_Reference: publication date Source_Citation_Abbreviation: VBMP 100 Scale DTM Source_Contribution: Digital terrain models Source_Information: Source_Citation: Citation_Information: Originator: VGIN Publication_Date: 200702 Title: 200 Scale Digital Terrain Models Geospatial_Data_Presentation_Form: vector digital data Online_Linkage: http://gisdata.virginia.gov Source_Scale_Denominator: 2400 Type_of_Source_Media: disc Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 200602 Ending_Date: 200704 Source_Currentness_Reference: publication date Source_Citation_Abbreviation: VBMP 200 Scale DTM Source_Contribution: Digital Terrain Models Process_Step: Process_Description: The analog aerial photography that was captured during the Spring of 2006/2007 was collected at 1"=600' scale for 2 foot contours and 1"=1200' for 4 foot contours. The aerial photography was controlled with strategic photo-identifiable ground control points. The ground control points were referenced to a Statewide adjusted Network in North American Datum 1983; Virginia State Plane North. The film was scanned at 14 microns and the aerotriangulation was executed using Zeiss ISAT software. The accuracy of the aerotriangulation data supports 1"=100' mapping and the generation of 2 foot contours or 1"=200' mapping and the generation of 4 foot contours. The Digital Terrain Model (DTM) and subsequent Triangular Irregular Network (TIN) used to the interpolate the contours was photogrammetrically stereo-compiled using fully manual methods to collect masspoints and breaklines. The initial base topo DTM was further densified for the creation of contours that will meet both NSSDA vertical accuracy specifications and FEMA contours specifications. The data was initially compiled in tile (2,500' X 2,500' or 5,000' X 5,000') format and seamlessly merged together into GeoDatabase format for final delivery. Source_Used_Citation_Abbreviation: VBMP 100 Scale DTM Source_Used_Citation_Abbreviation: VBMP 200 Scale DTM Process_Date: 200702Spatial_Data_Organization_Information: Direct_Spatial_Reference_Method: Vector Point_and_Vector_Object_Information: SDTS_Terms_Description: SDTS_Point_and_Vector_Object_Type: String Point_and_Vector_Object_Count: 46020Spatial_Reference_Information: Horizontal_Coordinate_System_Definition: Planar: Grid_Coordinate_System: Grid_Coordinate_System_Name: State Plane Coordinate System State_Plane_Coordinate_System: SPCS_Zone_Identifier: 4501 Lambert_Conformal_Conic: Standard_Parallel: 38.033333 Standard_Parallel: 39.200000 Longitude_of_Central_Meridian: -78.500000 Latitude_of_Projection_Origin: 37.666667 False_Easting: 11482916.666667 False_Northing: 6561666.666667 Planar_Coordinate_Information: Planar_Coordinate_Encoding_Method: coordinate pair Coordinate_Representation: Abscissa_Resolution: 1 Ordinate_Resolution: 1 Planar_Distance_Units: survey feet Geodetic_Model: Horizontal_Datum_Name: North American Datum of 1983 Ellipsoid_Name: Geodetic Reference System 80 Semi-major_Axis: 6378137.000000 Denominator_of_Flattening_Ratio: 298.257222 Vertical_Coordinate_System_Definition: Altitude_System_Definition: Altitude_Datum_Name: North American Vertical Datum of 1988 Altitude_Resolution: 0.000100 Altitude_Distance_Units: feet Altitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinatesEntity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: 2 and 4 Foot Contours