Town of Edgartown, MA GIS Viewer

Town of West Tisbury, MA GIS Viewer

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Town of Hubbardston, MA GIS Viewer

Best Practices Guide to Indoor Mapping, Tracking, and Navigation Version 1

The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International in January/February of 2015. This dataset consists of two combined mosaic datasets- one with 9 inch resolution imagery and one with 4 inch resolution imagery. This dataset is a referenced mosaic dataset with a slightly different projected coordinate system than the original. The original coordinate system was NAD83_North_Carolina_ft while the referenced projection is NAD_1983_StatePlane_North_Carolina_FIPS_3200_Feet. However, the projection and geographic coordinate systems are the same between datasets and are listed below.Projection: Lambert Conformal ConicFalse_Easting: 2000000.002616666000000000False_Northing: 0.000000000000000000Central_Meridian: -79.000000000000000000Standard_Parallel_1: 34.333333333333336000Standard_Parallel_2: 36.166666666666664000Latitude_Of_Origin: 33.750000000000000000Linear Unit: Foot_USMeters per unit: 0.304800609601219Name: GCS_North_American_1983Angular Unit: Degree (0.0174532925199433)Prime Meridian: Greenwich (0.0)Datum: D_North_American_1983Spheroid: GRS_1980Semimajor Axis: 6378137.0Semiminor Axis: 6356752.314140356Inverse Flattening: 298.257222101

Town of Otis, MA GIS Viewer

These shapefiles includes surficial geology, contacts, fault, and marker bed layers providing the legend for the surficial geology layer. Original data from 1940's-1960's. This database was developed to create a usable dataset of Kansas counties where no new mapping has taken place. It shows locations of geologic outcrops, contacts, and geologic structures in Kansas counties. This geologic data is that of the original geologic map and is the interpretation of the map's author. New information not included in this data may prove the interpretation to be incorrect. In addition, stratigraphic nomenclature used on the original map may not agree with current usage.Data is from the Kansas Geological Survey - Cartographic Services and its predecessors. The surficial geology layers display attributed polygons representing intervals in the stratigraphic sequence identified and mapped at the surface of the county. In the contacts layers of the database, contacts corresponding to the boundaries between adjacent geologic polygons on the map are represented by attributed line features. Marker bed layers include distinctive beds of rock strata that are easily distinguishable and observable over large horizontal distances. The surface expression of structural geologic features such as faults or the axis of a fold, syncline, or anticline are represented by attributed line features in the faults layers. Not all counties will have layers for all these features. Counties included are: Allen, Barton, Brown, Cheyenne, Clay, Cloud, Cowley, Decatur, Ellsworth, Franklin, Gove, Graham, Grant, Greeley, Harper, Haskell, Jackson, Kingman, Kiowa, Lane, Lincoln, Linn, Logan, Marshall, Meade, Miami, Mitchell, Nemaha, Ottawa, Pratt, Rawlins, Reno, Rice, Rush, Scott, Seward, Sheridan, Sherman, Stanton, Stevens, Sumner, Thomas, Trego, Wallace, Wichita

The starting point for creating these maps is to determine the axis of the fairway. This axis line is precisely plotted using a calculation in ArcGis. The bank lines as recorded in the current area GIS file were used. Given the irregular structure of the banks, this resulted in a rather angular midline, which is why this midline has been adjusted partly by generalisation and partly by hand. On the basis of this axis line, the minimum fairway widths have been plotted, as they apply to that specific route. This is calculated by plotting half of the required width to either side (buffing) from the center line. Where this width crosses the banks, infrastructural bottlenecks may arise. Where the waterway is wider than the minimum requirement, possible policy space arises. This is the space to further determine which function is assigned to it when elaborating. Different requirements apply to works of art, so other widths have also been set for this. To complete the whole, the maps have been supplemented with topography, kilometre measurement and nautical functions such as bollards, mooring chairs etc. On the basis of the ship dimensions, manual waiting and berth functions have been drawn up. A map layer has been created for each function. The functions are inventoried, recorded on management cards and manually recorded in Auto-Cad. The obtained files have been converted to ArcGIS.

The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International in January/February of 2013. This dataset consists of two combined mosaic datasets- one with 12 inch resolution imagery and one with 6 inch resolution imagery. This dataset is a referenced mosaic dataset with a slightly different projected coordinate system than the original. The original coordinate system was NAD83_North_Carolina_ft while the referenced projection is NAD_1983_StatePlane_North_Carolina_FIPS_3200_Feet. However, the projection and geographic coordinate systems are the same between datasets and are listed below.Projection: Lambert Conformal ConicFalse_Easting: 2000000.002616666000000000False_Northing: 0.000000000000000000Central_Meridian: -79.000000000000000000Standard_Parallel_1: 34.333333333333336000Standard_Parallel_2: 36.166666666666664000Latitude_Of_Origin: 33.750000000000000000Linear Unit: Foot_USMeters per unit: 0.304800609601219Name: GCS_North_American_1983Angular Unit: Degree (0.0174532925199433)Prime Meridian: Greenwich (0.0)Datum: D_North_American_1983Spheroid: GRS_1980Semimajor Axis: 6378137.0Semiminor Axis: 6356752.314140356Inverse Flattening: 298.257222101

The Danvers, MA - GIS provides the general public and other interested parties local government property tax and assessment information.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from http://pubs.usgs.gov/ds/0850/html/ds850_data.html.

City & Borough of Sitka Tax Parcel Viewer

Last Updated: 7/23/2024 by EWilhelm_Admin1 Field Listing: parcelid: Parcel APN IDtaxmapnumb: Tax map numbersitenum: Site address numbersiteroad: Site road nameparceladdr: Full parcel addresssitecity: Parcel"s cityunitbldg: Building unit numbersubdiv: Benicia sub-divisionrollyear: Current Tax Roll Yearacres: Measured acreagelotsize: Lot size in square feetLANDUSE: Land Use designationZONING: Zoning designationGENERAL_PLN: General plan designationShorLn_OVL: Shoreline overlay designationHistoricD: Historic District designationHistoricLM: Historic Land Mark designationCont_B: Contributing Building designationSix_L: Six L designationArsenalHD: Arsenal Historic District designationArsenalHL: Arsenal Historic Landmark designationArsenalCB: Arsenal Contributing Building designationArsenalPCB: Arsenal potential Contributing Building designationHSG_OPP: Housing OpportunitiesMills_Act: Mills Act, Yes or No?MA_MAIN: Mills Act Maintained, Yes or No?MA_Date: Date of Mills Actxcentroid: X axis centerycentroid: Y axis centerassessorma: URL link to Solano County Accessor Map PDFpropertych: URL Link to Solano County Property Chart PDFtaxinfo: URL Link to Solano County Tax Info PDFusecode: County use codeuse_desc: County use descriptionqclass: Quality Classyrblt: Year parcel was constructedstatus: PIN Statusvalland: Land Valuevalimp: Improvement Valuevaltv: Trees & Vine Valuevalfme: Fixed Machinery & Equipment Valuevalpp – Personal Property Valuevalpen: Penalty Valuesitus: Site Address (YS/NO)williamson: Williamson Act?wa_status: Williamson Act Status Codewa_contrac: Williamson Act Contract Numberwa_prime: Total Prime Acreagewa_nonprim: Total Nonprime Acreagewa_exclude: Total Excluded Acreagepcl_create: Parcel Creation Datepcl_inactd: Parcel Inactivation Datefirst_area: Property area 1second_are: Property area 2third_area: Property area 3other_area: Property area (Other)garage_are: Garage areatotal_area: Total property areastories: Amount of stories in the housebedroom: Amount of bedrooms in housebathroom: Amount of bathrooms in housedining: Dining room?family: Family room?other_room: Other rooms?utility: Utility spacetotal_room: Total rooms in homefireplc: Fire place?hvac: HVAC?pool: Pool on property?solar Solar on property?tac: TAGtac_city: TAG city namegovt_owned: Is parcel government owned?hotype: Home Owner Exemption Typezone1: Zoning Code #1 (this field is currently inactive and not being updated)zone2: Zoning Code #2 (this field is currently inactive and not being updated)z2acres: Zoning Code #2 Acreage (this field is currently inactive and not being updated)remark: Editor notedlndivnum: Land Division Number (this field is currently inactive and not being updated)lndivdate: Land Division Date (this field is currently inactive and not being updated)site_statu: Zoning Site Status (this field is currently inactive and not being updated)pudnum - Public Utility District Number (this field is currently inactive and not being updated)datevar – Date Verified (this field is currently inactive and not being updated)varnum – Variance Number (this field is currently inactive and not being updated)plfiltyp1 – File Type 1 (this field is currently inactive and not being updated)plfilno1 – File 1 Number (this field is currently inactive and not being updated)plfiltyp2 – File Type 2 (this field is currently inactive and not being updated)plfilno2 – File 2 Number (this field is currently inactive and not being updated)fund_fire – Fire Districtdesc_fire - Fire District Descriptionfund_school - School Districtdesc_school – School District Descriptionfund_water – Water Districtdesc_water – Water District Descriptionfund_air_board – Air Board Districtdesc_air_board - Air Board District Descriptionfund_soil_cons - Soil Conservation Districtdesc_soil_cons – Soil Conservation District DescriptionAcreage_Diff - Acreage DifferenceParcel2: ?Label: Field used to label parcelsHighestTide: Waterfront Residential Height LimitationAffd_HSing: Affordable Housing designationReal_Lease: Real estate Leasing Info

This is a GIS point feature shapefile representing wells, and their temperatures, that are located in the general Utah FORGE area near Milford, Utah. There are also fields that represent interpolated temperature values at depths of 200 m, 1000 m, 2000 m, 3000 m, and 4000 m. in degrees Fahrenheit.

The temperature values at specific depths as mentioned above were derived as follows. In cases where the well reached a given depth (200 m and 1, 2, 3, or 4 km), the temperature is the measured temperature. For the shallower wells (and at deeper depths in the wells reaching one or more of the target depths), temperatures were extrapolated from the temperature-depth profiles that appeared to have stable (re-equilibrated after drilling) and linear profiles within the conductive regime (i.e. below the water table or other convective influences such as shallow hydrothermal outflow from the Roosevelt Hydrothermal System). Measured temperatures/gradients from deeper wells (when available and reasonably close to a given well) were used to help constrain the extrapolation to greater depths.

Most of the field names in the attribute table are intuitive, however HF = heat flow, intercept = the temperature at the surface (x-axis of the temperature-depth plots) based on the linear segment of the plot that was used to extrapolate the temperature profiles to greater depths, and depth_m is the total well depth. This information is also present in the shapefile metadata.

May 2013 - performed by Kjeldsen, Sinnock & Neudeck Inc. (KSN) at the request of Reclamation District 2058 to assess bed elevation change versus 2004 and 2005 DWR surveys. The KSN survey consisted one single-beam sonar along-axis profile that included direct GPS observation points through the non-navigable, weed-filled eastern reach of the slough (starting at about cross section 8). The nine cross sections were surveyed with direct GPS observations of limited data density due to the method used.January 2018 – performed by DWR, North Central Region Office at the request of Bay Delta Office to assess channel elevations. This survey was intended to fully cover the target area with three along-axis profiles and the nine cross sections. All data was collected with sonar.The 2013 KSN dataset was very sparse in areas, with as much as 125 feet separating points along the profile, on average, and as few as five observations within the channel banks for cross sections. The 2018 DWR survey had much improved data density, but the comparison is necessarily limited by the availability of data from 2013. An estimate of bed elevation change in Tom Paine Slough along the centerline was made by subtracting 2013 elevation from calculated 2018 elevation for points that were within a 5-foot radius of one another according to an algorithm calculating the weighted average of nearest neighbor points (WANN). Change for the cross sections was analyzed separately. No attempt was made to produce an estimate of change for the whole of Tom Paine Slough because the data from 2013 only contained one centerline and the cross sections were too far apart to reasonably interpolate a surface. Change along the 2013 centerline was further broken down by 3,000 foot segments along the profile to examine change more locally. To prevent under-weighting areas with fewer qualifying data points, all statistics were calculated on an evenly-spaced dataset, with change in elevation interpolated to one foot intervals. Analysis of change along the centerline indicates a small, but significant, net deposition of sediment in Tom Paine Slough between 2013 and 2018. Sediment trends vary locally and are sensitive to differences in boat path 1, but suggest increasing deposition from west to east, with a short reach of scour near cross section 7. Since accuracy for two surveys was 0.24 ft and 0.16 ft for KSN and DWR, respectively, as much as 0.4 ft of difference could be explained by systematic survey error. However, this does not explain the increasing trend of deposition along Tom Paine Slough. The difference in data collection methods used by KSN — sonar for most of the slough, with direct GPS observation in weedy areas — could also be responsible for some of the increase seen towards the eastern end of the survey. There was a much higher mean elevation change for KSN points collected by direct observation (1.1 ft) compared to points collected by sonar (0.6 ft). Despite this, it is entirely possible that this observed difference between collection techniques was caused by an actual trend in deposition. It appears most likely that both are true: some of the increase in elevation towards the east is due to differing collection techniques, but most of it probably reflects an actual increase in elevation. Mean change in elevation and change in cross-sectional area was calculated for each individual cross section. Because the 2013 KSN cross sections were all conducted by labor intensive manual GPS observations, data points within the channel were sparse, but land elevations beyond the channel banks were included. The 2018 DWR survey was conducted by sonar within the navigable channel limits, and thus had much greater data density within the channel, but without land elevations beyond the banks.

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.

The global high-accuracy electronic digital theodolite market is experiencing robust growth, driven by increasing infrastructure development, surging demand for precise surveying and mapping solutions in construction, and the rising adoption of advanced technologies like GPS and GIS. The market, estimated at $850 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key trends, including the miniaturization and improved accuracy of theodolites, leading to wider adoption across diverse applications. Furthermore, the integration of digital technologies enhances data processing and analysis speeds, increasing efficiency and reducing project timelines. While the market faces certain restraints such as high initial investment costs and the availability of skilled labor to operate these sophisticated instruments, the overall market outlook remains positive due to the continuous advancements in the technology and the growing demand for precise measurements across various industries. The market segmentation reveals a strong preference for dual-axis theodolites over single-axis models, reflecting the need for comprehensive data acquisition in complex projects. Online sales channels are gaining traction, supplementing traditional offline sales, aided by increased e-commerce penetration. Major players like Hexagon, Trimble, Nikon, and Leica Geosystems are actively investing in research and development, expanding their product portfolios, and enhancing their global distribution networks to maintain their competitive edge. Regional analysis indicates strong growth in Asia Pacific, driven by rapid urbanization and infrastructure development in countries like China and India. North America and Europe also contribute significantly to the market, owing to robust construction and surveying activities. The forecast period (2025-2033) is expected to witness considerable growth, fueled by sustained investments in infrastructure and the evolving needs of various industries. This in-depth report provides a comprehensive analysis of the global high accuracy electronic digital theodolite market, projecting a market value exceeding $2 billion by 2030. It delves into market dynamics, competitive landscapes, and future growth trajectories, leveraging millions of data points to offer actionable insights for businesses, investors, and researchers. This report is optimized for high search volume keywords such as "electronic digital theodolite market," "high accuracy theodolite," "theodolite sales," and "geospatial surveying equipment."

Town of Warren, MA GIS Viewer

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. A spatial representation of five-foot topographic contour lines. This data used to create this line feature class was collected between 3/29/17 and 5/3/17. The 1-foot elevation contours were created on 12/13/18 by Axis Geospatial, and the County then created 5' contours from these. The 1' contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data that was collected as part of the State of Michigan's 2016 LiDAR project. The key attribute is Contour.