CDFW BIOS GIS Dataset, Contact: Charles Steinback, Description: This data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Spatial and Socioeconomic Change in the California Central Coast Commercial and CPFV Fisheries. This project is a component of the California Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the Central Coast Region since MPA implementation.

DOUGLAS COUNTY SURVEY/GISGIS PARCEL MAPPING GUIDELINES FOR PARCEL DISCREPANCIESIt is the intent of the Douglas County GIS Parcel Mapping to accurately identify the areas of land parcels to be valued and taxed 1. Discrepancies in areas• The Auditor/Assessor (tax) acreage areas started with the original US General Land Office (GLO) township plat maps created from the Public Land Survey (PLS) that was done between 1858 and 1871. The recovery of the PLS corners and the accurate location of these corners with GPS obtained coordinates has allowed for accurate section subdivisions, which results in accurate areas for parcels based on legal descriptions, which may be significantly different than the original areas. (See Example 2)• Any parcel bordering a meandered lake and/or a water boundary will likely have a disparity of area between the Auditor/Assessor acreages and the GIS acreages because of the inaccuracy of the original GLO meander lines from which the original areas were determined. Water lines are not able to be drafted to the same accuracy as the normal parcel lines. The water lines are usually just sketched on a survey and their dimensions are not generally given on a land record. The water boundaries of our GIS parcels are located from aerial photography. This is a subjective determination based on the interpretation by the Survey/GIS technician of what is water. Some lakes fluctuate significantly and the areas of all parcels bordering water are subject to constant change. In these cases the ordinary high water line (OHW) is attempted to be identified. Use of 2-foot contours will be made, if available. (See Example 1)• Some land records do not accurately report the area described in the land description and the description area is ignored. (See Example 3)• The parcel mapping has made every attempt to map the parcels based on available survey information as surveyed and located on the ground. This may conflict with some record legal descriptions.Solutions• If an actual survey by a licensed Land Surveyor is available, it will be utilized for the tax acreage.• If the Auditor/Assessor finds a discrepancy between the tax and GIS areas, they will request a review by the County Survey/GIS department.• As a starting guideline, the County Survey/GIS department will identify all parcels that differ in tax area versus GIS parcel area of 10 % or more and a difference of at least 5 acres. (This could be expanded later after the initial review.)• Each of these identified parcels will be reviewed individually by the County Survey/GIS department to determine the reason for the discrepancy and a recommendation will be made by the County Survey/GIS department to the Auditor/Assessor if the change should be made or not.• If a change is to be made to the tax area, a letter will be sent to the taxpayer informing them that their area will be changed during the next tax cycle, which could affect their property valuation. This letter will originate from the Auditor/Assessor with explanation from the County Survey/GIS department. 2. Gaps and Overlaps• Land descriptions for adjoining parcels sometimes overlap or leave a gap between them.o In these instances the Survey/GIS technician has to make a decision where to place this boundary. A number of circumstances are reviewed to facilitate this decision as these dilemmas are usually decided on a case by case basis. All effort will be made to not leave a gap, but sometimes this is not possible and the gap will be shown with “unknown” ownership. (Note: The County does not have the authority to change boundaries!)o Some of the circumstances reviewed are: Which parcel had the initial legal description? Does the physical occupation of the parcel line as shown on the air photo more closely fit one of the described parcels? Interpretation of the intent of the legal description. Is the legal description surveyable?Note: These overlaps will be shown on the GIS map with a dashed “survey line” and accompanying text for the line not used for the parcel boundary. 3. Parcel lines that do not match location of buildings Structures on parcels do not always lie within the boundaries of the parcel. This may be a circumstance of building without the benefit of a survey or of misinterpreting these boundaries. The parcel lines should be shown accurately as surveyed and/or described regardless of the location of structures on the ground. NOTE: The GIS mapping is not a survey, but is an interpretation of parcel boundaries predicated upon resources available to the County Survey/GIS department.Gary Stevenson Page 1 7/21/2017Example 1Example 2A Example 2B Example 3

CDFW BIOS GIS Dataset, Contact: Cheryl Chen, Description: These data were developed as part of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation.

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This dataset contains mean high water (MHW) shorelines for sandy beaches along the coast of California for the years 1998/2002, 2015, and 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method. The smoothed contour line was then quality controlled to remove artifacts, as well as remove any contour tool interpretation of human-made infrastructure (such as jetties, piers, and sea walls), using satellite imagery from ArcGIS.

Road surfaces were compiled by Axis Geospatial, LLC using aerial photography and LiDAR data captured by Pictometry Corp in 2013. Final delivery was April 2015. Road edges were compiled by surface type of concrete, asphalt, gravel, dirt, or under construction. The road surface was compiled to have all open intersections unless the surface type changed. Areas within the road network where surface type changes, a pavement change line connects the edge of the road at the change to the centerline at the change to the other side of the road edge at the change. Roads have precedence over parking areas and can be used for sides of parking areas. Ongoing updates to planimetrics are done based on Construction Plans, Building Permits, and updated aerial photography.

This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps).Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model (described in Vitousek and others, 2017; 2021; 2023) run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations. Shoreline positions from models are generated at pre-determined cross-shore transects and output includes different cases covering important model behaviors (cases are described in process steps of metadata; see citations listed in the Cross References section for more details on the methodology and supporting information). This model shows change in shoreline positions along transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This map includes change areas for city and county boundaries filed in accordance with Government Code 54900. The initial dataset was first published on October 20, 2021, and was based on the State Board of Equalization's tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax jurisdictions. The boundaries are continuously being revised when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions and should not be used to determine precise city or county boundary line locations.The data is updated within 10 business days of the CDTFA receiving a copy of the Board of Equalization's acknowledgement letter.BOE_CityAnx Data Dictionary: COFILE = county number - assessment roll year - file number (see note*); CHANGE = affected city, unincorporated county, or boundary correction; EFFECTIVE = date the change was effective by resolution or ordinance (see note*); RECEIVED = date the change was received at the BOE; ACKNOWLEDGED = date the BOE accepted the filing for inclusion into the tax rate area system; NOTES = additional clarifying information about the action.*Note: A COFILE number ending in "000" is a boundary correction and the effective date used is the date the map was corrected.BOE_CityCounty Data Dictionary: COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the Board of Equalization's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).

This dataset consists of rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each bluff line establishing measurement points, which are then used to calculate bluff-change rates.

This dataset consists of short-term (1970-2009) linear regression shoreline change rates for the South Cape Cod region of Massachusetts. Rates of short-term shoreline change were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate the short-term rates. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

This dataset consists of short-term (1970-2009) end point shoreline change rates for the South Cape Cod region of Massachusetts where there was not enough historic data to compute a linear regression rate (LRR). Rates of short-term shoreline change were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate the short-term rates. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

The global SF6 Gas Insulated Switchgear (GIS) market, valued at approximately $801 million in 2025, is projected to experience steady growth, driven by the increasing demand for reliable and efficient power transmission and distribution infrastructure across various sectors. The 2.9% Compound Annual Growth Rate (CAGR) from 2025 to 2033 indicates a consistent expansion, fueled primarily by the burgeoning renewable energy sector's need for advanced switchgear solutions. Growth in the electrical industry, transportation infrastructure development, and industrial automation are also contributing factors. The high voltage GIS segment is expected to dominate due to its application in large-scale power grids and transmission lines. Geographically, North America and Europe are anticipated to hold significant market share due to established power grids and substantial investments in grid modernization. However, rapid industrialization and infrastructure development in Asia Pacific, particularly in China and India, are expected to fuel strong growth in this region in the coming years. While the market faces challenges from environmental concerns regarding SF6's global warming potential, technological advancements are leading to the development of more eco-friendly alternatives and SF6 recycling solutions, mitigating these restraints. The adoption of smart grid technologies and increasing focus on improving grid reliability and resilience are key market trends. The rise of renewable energy sources, such as wind and solar, necessitates robust switchgear solutions that can handle fluctuating power generation and integrate renewable energy seamlessly into existing grids. Furthermore, stringent safety regulations and the growing awareness of environmental sustainability are influencing the market towards safer, more environmentally conscious alternatives to traditional SF6-based GIS. Competition among major players like ABB, Siemens, and GE is intense, driving innovation and cost optimization within the market. Ongoing research and development efforts are focusing on improving efficiency, reducing the environmental impact, and enhancing the safety features of SF6 GIS. This will ensure continued market growth and evolution over the forecast period.

This dataset consists of long-term (100+ years) linear regression shoreline change rates for the South Shore region of Massachusetts. Rates of long-term shoreline change were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate a linear regression rate for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Long-term linear regression statistics were calculated with all of the historical shorelines compiled for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

Various historical maps from Vantaa. More detailed information about the different maps can be found in the Layers descriptions section.

The material can be viewed in the City of Vantaa map service:

• The levels can be found in the map service by searching for 'Other historical maps'.

Coordination system(s):

• ETRS-GK25 (EPSG:3879)

Addresses for cross-border services:

• WMS: https://gis.vantaa.fi/geoserver/wms?request=GetCapabilities
• Description of the interface service: https://gis.vantaa.fi/interfaces/

Published levels:

• history:land species map_mv
• history:vantaantilat_1983
• history:Tikkurila_1950_construction plan
• history:longitudinal map_1933
• history:longitudinal map_1749
• history:Senate map_1872
• History: King's Map

Level descriptions

Name: Land species map (black and white)

In the soil species map, the soil is mapped to a depth of about one metre. For soil layers deeper than this, the soil type map does not provide information. The mapping of soil types has been carried out on a scale of 1:2,000 or 1:10,000, so the smallest soil types have not been presented. The soil species do not change unambiguously at the boundary line shown on the maps, but rather the boundary line represents the zone of change of soil species.

Timeliness of data: The soil species map was made mainly in the 1980s and represents the conditions of that time. The map has not been updated.

Explanation of abbreviations in the country map:

• Ka or dark raster = rock covered by an open or thin layer of loose soil
• Mr = moraine soil species
• Sr = gravel
• Hk = sand
• Si = silt (previously silt/sand)
• Sa = clay
• Lj = mud
• Tv = peat
• T = area where excavation or filling has taken place

Double marking means that the topsoil species changes to another at a depth of less than one metre from the ground surface. For example, Hk/Sa means that the surface layer is sand to a depth of no more than 0.9 m and the soil is clay to a depth of 1 m.

Name: Facilities in Vantaa

The dataset includes Vantaa's premises according to the time of 1983. In areas where there is no local detailed plan, construction is regulated by the local master plan. The provisions of the master plan tie the number of dwellings to the surface area of the premises at the time of the adoption of the 1983 master plan (6.6.1983). The regulations apply to the detached house areas A4, village areas AT, agricultural areas MT and areas M, which are dominated by agriculture and forestry.

Name: Construction plan for Tikkurila 1950

Map of the construction plan area of Tikkurila in the villages of Tikkurila, Suutarinkylä and Hakkila in the city of Helsinki and in the rural municipality of Uusimaa.

Construction plan surveyor Niilo Tarkka, surveyor in 1937-47. The survey was completed in 1947-50 by surveyor J. Rauniomäki.

Name: Keeper's Map 1933

The National Land Survey of Finland prepared the parish map 1:20 000 between 1825 and 1950. The production took place by parish in 1825–1915 and by map sheet in 1916–1950. From 1927 onwards, the parish maps were published as 10 km x 10 km magazines in the so-called general magazine division.

Name: Keeper map 1749

Friedrich Johan Fonseen's map of the parish of Helsinki from 1749.

Source: Krigsarkivet (Sverige) - War Archive (Sweden). The city's spatial data team has put it into the current coordinate system.

Name: Senate map 1872

The map is based on surveys made by the topographical department of the Russian Ministry of War in 1870-1907 at a scale of 1:21,000 about the southern part of the Pori-Käkisalmi line.

Name: King's Map

The King's Atlas 1776-1805

In 1776-1805, an extensive military survey, the so-called recognition survey, was carried out in Finland. According to the work instructions, the maps had to be drawn up so accurately that no militarily significant terrain would be overlooked. With the help of maps, the warlord had to be able to plan both offensive and defensive actions without knowing the terrain. As a result, the maps depict e.g. roads (including winter roads), water routes, rustolles, crofts and vicarages.

The maps are based on older geometric maps, in which the mappers both supplemented and corrected the data while working in the field. In addition, the mappers have in some cases used the help of local residents to find out the roads, terrain and the name of the locality. The language used in the map is Swedish. The original hand-drawn maps are stored at the Swedish State Archives in Stockholm.

Source: Krigsarkivet, Finska rekognosceringsverket.

[Metadata] TMK Parcel boundaries for the County of Kauai, including the islands of Kauai, Niihau, Lehua and Kaula as of May, 2024. Source: Kauai County. The parcel boundaries are intended to provide a visual reference only and do not represent legal or survey level accuracy. Attributes are for assessment purposes only per the Real Property Department and are subject to change at any time.NOTE: This layer is maintained by the County of Kauai. It is downloaded by the Statewide GIS Program for integration into the Statewide parcel layer, and provided to State GIS users as a convenience and on various State of Hawaii sites as a service to the public. FOR THE MOST CURRENT VERSION OF THE COUNTY OF KAUAI PARCELS, VISIT THE COUNTY OF KAUAI OPEN DATA HUB: https://kauai-open-data-kauaigis.hub.arcgis.com/. Previous Update of the Kauai TMK Parcel Layer 2013 - Spatial Shift using NavTeq Roads,NOAA Charts and Imagery to anchor shift with 1100 displacement points and Affine Shift. After original shift it was done as a spatial rubbersheet natural Neighbor shift.For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/niparcels.pdf or contact the Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.Kauai County Disclaimer:The Geographic Information Systems (GIS) maps and data are made available solely for informational purposes. The GIS data is not the official representation of any of the information included, and do not replace a site survey or legal document descriptions. The County of Kauai (County) makes or extends no claims, representations or warranties of any kind, either express or implied, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose, as to the quality, content, accuracy, currency, or completeness of the information, text, maps, graphics, links and other items contained in any of the GIS data. In no event shall the County become liable for any errors or omissions in the GIS, and will not under any circumstances be liable for any direct, indirect, special, incidental, consequential, or other loss, injury or damage caused by its use or otherwise arising in connection with its use, even if specifically advised of the possibility of such loss, injury or damage. The data and or functionality on this site may change periodically and without notice. In using the GIS data, users agree to indemnify, defend, and hold harmless the County for any and all liability of any nature arising out of or resulting from the lack of accuracy or correctness of the data, or the use of the data. The parcel boundaries are intended to provide a visual reference only and do not represent legal or survey level accuracy. Attributes are for assessment purposes only per the Real Property Department and are subject to change at any time.

description: The shoreline of Cape Hatteras, North Carolina, is experiencing long-term coastal erosion. In order to better understand and monitor the changing coastline, historical aerial imagery is used to map shoreline change. For the area of Hatteras Island from Cape Point to Oregon Inlet, fourteen aerial datasets from 1978-2002 were scanned and georeferenced for use in a Geographic Information System (GIS). Shoreline positions (high water line) were digitized from georeferenced imagery. The shoreline vectors were then compiled for use in the Digital Shoreline Analysis System (DSAS) ArcGIS extension in order to generate rates of shoreline change.; abstract: The shoreline of Cape Hatteras, North Carolina, is experiencing long-term coastal erosion. In order to better understand and monitor the changing coastline, historical aerial imagery is used to map shoreline change. For the area of Hatteras Island from Cape Point to Oregon Inlet, fourteen aerial datasets from 1978-2002 were scanned and georeferenced for use in a Geographic Information System (GIS). Shoreline positions (high water line) were digitized from georeferenced imagery. The shoreline vectors were then compiled for use in the Digital Shoreline Analysis System (DSAS) ArcGIS extension in order to generate rates of shoreline change.

This dataset consists of long-term (100+ years) linear regression shoreline change rates for the Boston region of Massachusetts. Rates of long-term shoreline change were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. The baseline is used as a reference line for the transects cast by the DSAS software. The transects intersect each shoreline at the measurement points, which are then used to calculate a linear regression rate for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Long-term linear regression statistics were calculated with all of the historical shorelines compiled for the Massachusetts Office of Coastal Zone Management Shoreline Change Project. Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

This dataset includes shorelines from 24 years ranging from 1869 to 2002 in Washington's coastal region. Shorelines were compiled from historic maps called T-sheets (NOAA), air photos (Washington Department of Ecology), and lidar (USGS/NASA). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) National Assessment Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.2. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates. Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner..

This dataset includes shorelines from 167 years ranging from 1848 to 2014 within the Cape Cod Bay coastal region of Massachusetts. Shorelines were compiled from T-sheets and air-photos obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), and lidar obtained from the US Geological Survey (USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the MA CZM Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate-of-change statistics. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010-2014. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management (MA CZM) and USGS historical shoreline data to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS). For publication purposes, the shoreline data for Massachusetts were organized by region in order match the extent of previously published uncertainty files used in shoreline change calculations.

This dataset depicts contours of estimated change in groundwater piezometric surfaces in the unconfined or uppermost semi-confined aquifers, between two specified years, by season. Contours represent change in groundwater level (elevation) by year and season (fall or spring). The contour interval is 10 ft. The contours represent lines of equal change in groundwater level surface. Positive values indicate groundwater has risen (groundwater surface elevation has increased) from the early year to the late year, while negative values indicate groundwater level surface has fallen (decreased in elevation ) from the early year to the late year. For 'Elevation' type contours, contours represent lines of equal elevation of the estimated groundwater level surface above mean sea level. Higher contour values indicate higher elevations of the estimated groundwater level. Water level measurements used for contouring are selected based on measurement date and well construction information, where available, and approximate groundwater levels in the unconfined to uppermost semi-confined aquifers.