The Virginia Water Protection Program (VWP) regulates activities in all surface waters which includes wetlands, streams, and open water. A permit (general permit coverage or individual permit) may be required in accordance with program regulations and State Water Control Law. This layer represents the project location of both active and historic VWP Individual Permits (IP) issued across the state. The Individual Permit's number, name, and regional office are among several attributes in this layer. The attribute data is pulled from DEQ's Comprehensive Environmental Data System (CEDS). The GIS data layers and maps produced by the Virginia Department of Environmental Quality (DEQ) are provided as a public resource. The Virginia Department of Environmental Quality has attempted to ensure the accuracy and completeness of the data. DEQ makes or extends no warranties of any type, expressed or implied, including but not limited to: appropriateness of use, accuracy, or completeness of data. DEQ does not guarantee the GIS data provided is complete or current because the information contained in these data may change over time. These data and related maps are not legal documents and should not be construed or used as such. It is encouraged that these data are obtained from a DEQ resource and not through other sources which may lead to changed or outdated data. While using DEQ GIS data, the use and modification of such data is done at the risk of the user, and where DEQ holds no liability of any nature resulting from the use or correctness of this data. Pursuant to Section 54.1-402, paragraph C of the Code of Virginia, this data may not be used for purposes listed in said paragraph C, since the data was not created, nor is it maintained under the supervision of a licensed land surveyor.Click Here to view Data Fact Sheet.

This geodataabase provides an estimate to the spatial distribution of potential historical habitat for California Coastal Chinook Salmon, Central California Coast Coho Salmon, Northern California Steelhead and Central California Coast Steelhead. Intrinsic potential measures the potential for development of favorable habitat characteristics as a function of the underlying geomorphic and hydrological attributes, as determined through a Digital Elevation Model (DEM) and mean annual precipitation grid. The model does not predict the actual distribution of "good'' habitat, but rather the potential for that habitat to occur, nor does the model predict abundance or productivity. Additionally, the model does not predict current conditions, but rather those patterns expected under pristine conditions as related through the input data. Thus, IP provides a tool for examining the historical distribution of habitat among and within watersheds, a proxy for population size and structure, and a useful template for examining the consequences of recent anthropogenic activity at landscape scales.

CDFW BIOS GIS Dataset, Contact: Charleen Gavette, Description: Information on estimating the quality and extent of historical habitat is needed for ongoing efforts to conserve and eventually recover protected populations of Pacific salmonids. Because georeferenced spatial data on habitat and fish distribution at the regional scale are lacking, we adapted methods of the Coastal Landscape Analysis and Modeling Study (CLAMS) to implement a GIS approach in modeling the intrinsic potential (IP) of stream reaches to support juvenile steelhead, coho, and chinook.

Planning an offshore wind project is considered a highly complex and multivariable task since it involves controversial objectives and constraints to be considered. Hence, compactness and contiguity are indispensable properties in spatial modelling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) site-prospecting multi-objective optimization in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP), towards an integer mathematical programming approach.

CDFW BIOS GIS Dataset, Contact: Charleen Gavette, Description: Information on estimating the quality and extent of historical habitat is needed for ongoing efforts to conserve and eventually recover protected populations of Pacific salmonids. Because georeferenced spatial data on habitat and fish distribution at the regional scale are lacking, we adapted methods of the Coastal Landscape Analysis and Modeling Study (CLAMS) to implement a GIS approach in modeling the intrinsic potential (IP) of stream reaches to support juvenile steelhead, coho, and chinook.

This geospatial dataset delivers high-accuracy GPS event streams from millions of connected devices across Asia, enabling advanced mobility, mapping, and location intelligence applications. Sourced from tier-1 app developers and trusted suppliers, it provides granular insights for commercial, government, and research use.

Each record includes: Latitude & Longitude coordinates Event timestamp (epoch & date) Mobile Advertising ID (IDFA/GAID) Horizontal accuracy (~85% fill rate) Country code (ISO3) Optional metadata: IP address, carrier, device model

Access & Delivery API with polygon queries (up to 10,000 tiles) Formats: JSON, CSV, Parquet Delivery via API, AWS S3, or Google Cloud Storage Hourly or daily refresh options Historical backfill from September 2024 Credit-based pricing for scalability

Compliance Fully compliant with GDPR and CCPA, with clear opt-in/out mechanisms and transparent privacy policies.

Use Cases Advanced mapping and GIS solutions Urban mobility and infrastructure planning Commercial site selection and market expansion Geofencing and targeted advertising Disaster response planning and risk assessment Transportation and logistics optimization

Large wood recruitment opportunity modeling in Campbell Creek planning watershed as part of the CA Timber Regulation and Forest Restoration Program's Campbell Creek Pilot Project

SGN create 4 separate data layers (by pressure tier) to depict the location of their gas network: LP - Low Pressure (19 mbar - 75 mbar) MP - Medium Pressure (75mbar - 2 bar) IP - Intermediate Pressure (2 bar - 7 bar) HP - Regional High Pressure (>7 bar) In order to assess the risk of planning applications affecting the gas infrastructure, there is a requirement to buffer the above pipe network by different distances. The Spatial Hub has provided these buffered polygons as separate data layers. These are consultation zones where SGN should be informed about relevant planning applications. Major developments with potentially widespread effects should have larger consultation distances of up to 1km: e.g. quarrying, deep mining, demolition, blasting, siting of High Voltage Infrastructure. Current buffers: HP buffer (75m either side of pipe - 150m total) HP buffer (1km either side of pipe - 2km total) - for assessment of large, major-scale works involving quarrying, demolition or power generation. IP buffer (25m either side of pipe - 50m total) MP buffer (12.5m either side of pipe - 25m total) The gas network data is up to date at the time of publication, but it is given without warranty as to the accuracy of the information shown. Service pipes, valves, siphons, sub-connections etc. are not shown but their presence should be anticipated. No liability of any kind whatsoever is accepted by SGN or its agents, servants or sub-contractors for any error or omission. Should the user wish to excavate in the vicinity of pipelines, the User should visit SGN via sgn.co.uk/Safety/Dig-safely for further information. SGN use an on-line mapping system, accessible via the sgn.co.uk/Safety/Dig-safely web pages or linesearchbeforeudig.co.uk, this process should be used to obtain up to date maps and safety information before you excavate. However if you need more information please contact our Safety Admin team on 0800 912 1722 or by email: plantlocation@sgn.co.uk. For the avoidance of doubt, safe digging practices, in accordance with HS (G) 47, must be used to verify and establish the actual position of mains, pipes, services and other apparatus on site before any mechanical plant is used. It is your responsibility to ensure that this information is provided to all persons (whether direct labour or contractors) working for you on or near gas apparatus. Mains shown in the data are those owned by SGN by virtue of being a licensed Gas Transporter (GT). Gas pipes owned by other GT’s, or third parties, may also be present in the area and are not shown in the data. Information with regard to such pipes should be obtained from the relevant owners

Data compiled is the distribution of native vegetation within the Cessnock Local Government Area rated against key criteria to determine its World Heritage value. The data provides a revision of existing vegetation mapping with scored sensitivity values for a range of criteria, used to derive a sensitivity index. This will aid in the determination of areas that may be suitable for World Heritage listing throughout the Cessnock Local Government Area.

• The GIS mapping data were obtained to inform sustainability planning for the Lower Hunter region, particularly to assist in informing the NSW Government review of the NSW Lower Hunter regional strategy and Lower Hunter regional conservation plan.
• Once this review is complete, the second stage is to undertake a strategic assessment of proposed urban development and related infrastructure corridors. The NSW Government has entered into an agreement with the Commonwealth to conduct a strategic assessment of proposed urban development and related infrastructure corridors.
• While reasonable efforts have been made to ensure that the contents of this dataset are factually correct, the Commonwealth does not accept responsibility for the accuracy or completeness of the contents, and shall not be liable for any loss or damage that may be occasioned directly or indirectly through the use of, or reliance on, the contents of this dataset.CC - Attribution (CC BY) This work is licensed under a Creative Commons Attribution 3.0 Australia License. Data to be available in the Public Domain under Creative Commons by Attribution Licencing Agreement. More information can be found here: http://creativecommons.org/licenses/by/3.0/au/deed.en IP owned by IP by Department of the Environment and Energy. The data is provided as is and was captured and manipulated using best practice. Parsons Brinckerhoff bear no responsibility should the data be found to be incorrect. Users must attribute the Australian Government Department of the Environment and Energy as the data provider. © Commonwealth of Australia (Department of the Environment and Energy) 2014

Feature class that compares the elevations between sand dune crests (extracted from available LiDAR datasets from 2010 and 2013) with published FEMA Base Flood Elevations (BFEs) from preliminary FEMA DFIRMS (Panels issued in 2018 and 2019) in coastal York and Cumberland counties (up through Willard Beach in South Portland). Steps to create the dataset included:Shoreline structures from the most recent NOAA EVI LANDWARD_SHORETYPE feature class were extracted using the boundaries of York and Cumberland counties. This included 1B: Exposed, Solid Man-Made structures, 8B: Sheltered, Solid Man-Made Structures; 6B: Riprap, and 8C: Sheltered Riprap. This resulted in the creation of Cumberland_ESIL_Structures and York_ESIL_Structures. Note that ESIL uses the MHW line as the feature base.Shoreline structures from the work by Rice (2015) were extracted using the York and Cumberland county boundaries. This resulted in the creation of Cumberland_Rice_Structures and York_Rice_Structures.Additional feature classes for structures were created for York and Cumberland county structures that were missed. This was Slovinsky_York_Structures and Slovinsky_Cumberland_Structures. GoogleEarth imagery was inspected while additional structures were being added to the GIS. 2012 York and Cumberland County imagery was used as the basemap, and structures were classified as bulkheads, rip rap, or dunes (if known). Also, whether or not the structure was in contact with the 2015 HAT was noted.MEDEP was consulted to determine which permit data (both PBR and Individual Permit, IP, data) could be used to help determine where shoreline stabilization projects may have been conducted adjacent to or on coastal bluffs. A file was received for IP data and brought into GIS (DEP_Licensing_Points). This is a point file for shoreline stabilization permits under NRPA.Clip GISVIEW.MEDEP.Permit_By_Rule_Locations to the boundaries of the study area and output DEP_PBR_Points.Join GISVIEW.sde>GISVIEW.MEDEP.PBR_ACTIVITY to the DEP_PBR_Points using the PBR_ID Field. Then, export this file as DEP_PBR_Points2. Using the new ACTIVITY_DESC field, select only those activities that relate to shoreline stabilization projects:PBR_ACTIVITY ACTIVITY_DESC02 Act. Adjacent to a Protected Natural Resource04 Maint Repair & Replacement of Structure08 Shoreline StabilizationSelect by Attributes > PBR_ACTIVITY IN (‘02’, ‘04’, ‘08’) select only those activities likely to be related to shoreline stabilization, and export the selected data as a DEP_PBR_Points3. Then delete 1 and 2, and rename this final product as DEP_PBR_Points.Next, visually inspect the Licensing and PBR files using ArcMap 2012, 2013 imagery, along with Google Earth imagery to determine the extents of armoring along the shoreline.Using EVI and Rice data as indicators, manually inspect and digitize sections of the coastline that are armored. Classify the seaward shoreline type (beach, mudflat, channel, dune, etc.) and the armor type (wall or bulkhead). Bring in the HAT line and, using that and visual indicators, identify whether or not the armored sections are in contact with HAT. Use Google Earth at the same time as digitizing in order to help constrain areas. Merge digitized armoring into Cumberland_York_Merged.Bring the preliminary FEMA DFIRM data in and use “intersect” to assign the different flood zones and elevations to the digitized armored sections. This was done first for Cumberland, then for York Counties. Delete ancillary attributes, as needed. Resulting layer is Cumberland_Structure_FloodZones and York_Structure_FloodZones.Go to NOAA Digital Coast Data Layers and download newest LiDAR data for York and Cumberland county beach, dune, and just inland areas. This includes 2006 and newer topobathy data available from 2010 (entire coast), and selected areas from 2013 and 2014 (Wells, Scarborough, Kennebunk).Mosaic the 2006, 2010, 2013 and 2014 data (with 2013 and 2014 being the first dataset laying on top of the 2010 data) Mosaic this dataset into the sacobaydem_ftNAVD raster (this is from the MEGIS bare-earth model). This will cover almost all of the study area except for armor along several areas in York. Resulting in LidAR206_2010_2013_Mosaic.tif.Using the LiDAR data as a proxy, create a “seaward crest” line feature class which follows along the coast and extracts the approximate highest point (cliff, bank, dune) along the shoreline. This will be used to extract LiDAR data and compare with preliminary flood zone information. The line is called Dune_Crest.Using an added tool Points Along Line, create points at 5 m spacing along each of the armored shoreline feature lines and the dune crest lines. Call the outputs PointsonLines and PointsonDunes.Using Spatial Analyst, Extract LIDAR elevations to the points using the 2006_2010_2013 Mosaic first. Call this LidarPointsonLines1. Select those points which have NULL values, export as this LiDARPointsonLines2. Then rerun Extract Values to Points using just the selected data and the state MEGIS DEM. Convert RASTERVALU to feet by multiplying by 3.2808 (and rename as Elev_ft). Select by Attributes, find all NULL values, and in an edit session, delete them from LiDARPointsonLines. Then, merge the 2 datasets and call it LidarPointsonLines. Do the same above with dune lines and create LidarPointsonDunes.Next, use the Cumberland and York flood zone layers to intersect the points with the appropriate flood zone data. Create ….CumbFIRM and …YorkFIRM files for the dunes and lines.Select those points from the Dunes feature class that are within the X zone – these will NOT have an associated BFE for comparison with the Lidar data. Export the Dune Points as Cumberland_York_Dunes_XZone. Run NEAR and use the merged flood zone feature class (with only V, AE, and AO zones selected). Then, join the flood zone data to the feature class using FID (from the feature class) and OBJECTID (from the flood zone feature class). Export as Cumberland_York_Dunes_XZone_Flood. Delete ancillary columns of data, leaving the original FLD_ZONE (X), Elev_ft, NEAR_DIST (distance, in m, to the nearest flood zone), FLD_ZONE_1 (the near flood zone), and the STATIC_BFE_1 (the nearest static BFE).Do the same as above, except with the Structures file (Cumberland_York_Structures_Lidar_DFIRM_Merged), but also select those features that are within the X zone and the OPEN WATER. Export the points as Cumberland_York_Structures_XZone. Again, run the NEAR using the merged flood zone and only AE, VE, and AO zones selected. Export the file as Cumberland_York_Structures_XZone_Flood.Merge the above feature classes with the original feature classes. Add a field BFE_ELEV_COMPARE. Select all those features whose attributes have a VE or AE flood zone and use field calculator to calculate the difference between the Elev_ft and the BFE (subtracting the STATIC_BFE from Elev_ft). Positive values mean the maximum wall value is higher than the BFE, while negative values mean the max is below the BFE. Then, select the remaining values with switch selection. Calculate the same value but use the NEAR_STATIC_BFE value instead. Select by Attributes>FLD_ZONE=AO, and use the DEPTH value to enter into the above created fields as negative values. Delete ancilary attribute fields, leaving those listed in the _FINAL feature classes described above the process steps section.

King County fish passage barrier prioritization scoring and Level B assessment data processing requires site-specific drainage basins. This dataset includes the area of land where precipitation collects and drains to a particular site. These drainage areas are used to calculate a variety of prioritization metrics, including percent impervious and forest land cover, count and density of upstream barriers, plus sum of upstream stream length and IP score. For sites where a Level B assessment was performed, the drainage area is used to calculate the low and high flow exceedence flows required to determine if a site is a barrier.

Information on estimating the quality and extent of historical habitat is needed for ongoing efforts to conserve and eventually recover protected populations of Pacific salmonids. Because georeferenced spatial data on habitat and fish distribution at the regional scale are lacking, we requested that NOAA implement a GIS approach in modeling the intrinsic potential (IP) of stream reaches to support juvenile coho salmon. The IP model uses geomorphic and hydrological attributes to estimate the latent potential of stream reaches to provide favorable habitat characteristics for spawning and rearing. Indices for the model are derived from a DEM and precipitation data. Thus, the model predicts patterns of relative productive potential expected in the absence of human disturbances, as related through the input data. The stream lines used as the basis for the IP geometry are based on King County's watercourse dataset, which has omission and spatial accuracy issues.

The Upper Wenatchee Pilot Project GIS Geodatabase associated with the Aquatics Report done by Cramer Fish Sciences in 2019.

The Office of Performance Improvement strives to provide Metro Government and its partners with customized improvement support to create a world-class city.View the dashboardData Dictionary:• Entry Id – Entry Id• Name – First name• Last – Last name• Full Email – Email address• Phone Number - Phone number• Zip Code – Zip Code• Organization - Organization• Support public health expenditures - By funding COVID-19 mitigation efforts, medical expenses, behavioral healthcare, and certain public health and safety staff.Address negative economic impacts caused by the public health emergency Including economic harms to workers, households, small businesses, impacted industries, and the public sector.Replace lost public sector revenue - Using this funding to provide government services to the extent of the reduction in revenue experienced due to the pandemic.Provide premium pay for essential workers - Offering additional support to those who have borne and will bear the greatest health risks because of their service in critical infrastructure sectors.Invest in water, sewer, and broadband infrastructure - Making necessary investments to improve access to clean drinking water, support vital wastewater and storm water infrastructure, and to expand access to broadband internet.• Comments – Comments• Date Created – Date comment form was submitted• Created By – Created by Public• Last Updated –Comment was updated• Updated By – Who updated comment• IP Address – IP Address Redacted• Last Page Accessed – The last page of form accessed• Completion Status – Comment form was submittedContact:Dwjuan McDonalddwjuan.mcdonald@louisvilleky.gov

Attachment regarding request by Brad Haertling, P.E. on behalf of Contentnea Creek for subdivision Construction Plan review of Summit Terrace, consisting of 26 lots on 56.15 acres, located off Mt. Gilead Church Road (SR-1700), parcel 19355.

Activitats organitzades a la Biblioteca Municipal de Manlleu BBVA (sessions) i públic assistent.- Font: Biblioteca Municipal BBVA - Actualització: anual - Formats: