Town of Gill, MA GIS Viewer

Town of Deerfield, MA GIS Viewer

A web map used to visualize available digital parcel data for Organized Towns and Unorganized Territories throughout the state of Maine. Individual towns submit parcel data on a voluntary basis; the data are compiled by the Maine Office of GIS for dissemination by the Maine GeoLibrary, and where available, the web map also includes assessor data contained in the Parcels_ADB related table.This web map is intended for use within the Maine Geoparcel Viewer Application; it is not intended for use as a standalone web map.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, sometimes many years apart, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.

Town of Freetown, 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 Sheffield, MA GIS Viewer

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

Town of Northbridge, MA GIS Viewer

Publication Date: April 2025. The Westchester County Parcels layer contains 2023- 2024 parcel data only for the County which gave NYS ITS Geospatial Services permission to share this data with the public. No attempt was made to edge-match parcels along adjacent counties. County attribute values were populated using 2024 Assessment Roll tabular data Geospatial Services obtained from the NYS Department of Tax and Finance’s Office of Real Property Tax Services (ORPTS).Tabular assessment data was joined to the county provided parcel geometry using the SWIS & SBL or SWIS & PRINT KEY unique identifier for each parcel. Detailed information about assessment attributes can be found in the ORPTS Assessor’s Manuals available here: https://www.tax.ny.gov/research/property/assess/manuals/assersmanual.htm. This map service is available to the public. The State of New York, acting through the New York State Office of Information Technology Services, makes no representations or warranties, express or implied, with respect to the use of or reliance on the Data provided. The User accepts the Data provided “as is” with no guarantees that it is error free, complete, accurate, current or fit for any particular purpose and assumes all risks associated with its use. The State disclaims any responsibility or legal liability to Users for damages of any kind, relating to the providing of the Data or the use of it. Users should be aware that temporal changes may have occurred since this Data was created.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

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.

Axes des routes / StrassenachsenMetadaten / Métadonnées

Town of North Attleborough, MA GIS Viewer

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 July 2013. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to understand better 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 interacts with and affects the morphologic evolution of the barrier-island system. Data collected from this study can 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 prior sampling efforts can 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 July 2013 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 https://pubs.usgs.gov/ds/894/downloads.html.

Underlying dimensions of landscape value assessment using principal axis factoring extraction method and Promax Rotation with Kaiser Normalization (KMO = 0.855, the significance level of Bartlett’s Test of Sphericity is 0.000, total variance explained is 53.725%).

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.

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."

Within the framework of the Rural Development Programming 2007-2013, Aquitaine can mobilise European funding to implement structuring and innovative projects for local development in rural and peri-urban areas, through the LEADER (Liaison Entre Actions de Développement de l’Economie Rurale) programme. After 3 generations of Community initiative programmes (LEADER I, LEADER II and LEADER+), LEADER IV is axis 4 of the rural development programme for hexagon (HRDP). The PDRH determines the measures of the European Rural Development Programme open to the 21 regions of metropolitan France outside Corsica. Within the PDRH, Axis 4 LEADER makes it possible to implement measures under Axis 1 (improving the competitiveness of the agricultural and wine sectors), 2 (improvement of the environment and rural areas) and 3 (improvement of the quality of life and diversification of economic activities in rural areas), combining them and adapting them to the profile of local territories, as part of a local development strategy. The LEADER programme 2007-2013 is an innovative approach to the implementation of local development strategies dedicated to organised rural areas, carrying out a project covering several sectors of the rural economy and involving private and public actors, brought together within a Local Action Group (LAG). Each LAG is identified by means of a precise list of municipalities, and aims to implement a “targeted priority”, i.e. a strategy developed through a development plan. The LAG is responsible for the development and implementation of the strategy of the LEADER programme in its territory through a programming committee with at least 50 % private members. The actions programmed must provide added value in terms of methodology (new partnerships) or content (impacts on the territory). Projects carried out under LEADER are also intended to be exemplary and disseminated in order to serve as examples for other rural areas, in particular through cooperation activities.

WMS and WFS addresses: Warnings — Please delete any spaces that might appear when copying/pasting the address into the GIS software — Problems with displaying multi polygons via the use of WFS (under resolution) — prefer data download if presence of multi polygons — WFS display of more than 500 objects via the WFS impossible at the moment

WMS address for integration into a GIS from Geoide_Carto: http://data.geo-ide.application.developpement-durable.gouv.fr/WMS/228/GAL_R72?

WFS address for GIS integration: http://ogc.geo-ide.developpement-durable.gouv.fr/cartes/mapserver?map=/opt/data/carto/geoide-catalogue/REG072/JDD.www.map

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.