This dataset represents point locations of cities and towns in Arizona. The data contains point locations for incorporated cities, Census Designated Places and populated places. Several data sets were used as inputs to construct this data set. A subset of the Geographic Names Information System (GNIS) national dataset for the state of Arizona was used for the base location of most of the points. Polygon files of the Census Designated Places (CDP), from the U.S. Census Bureau and an incorporated city boundary database developed and maintained by the Arizona State Land Department were also used for reference during development. Every incorporated city is represented by a point, originally derived from GNIS. Some of these points were moved based on local knowledge of the GIS Analyst constructing the data set. Some of the CDP points were also moved and while most CDP's of the Census Bureau have one point location in this data set, some inconsistencies were allowed in order to facilitate the use of the data for mapping purposes. Population estimates were derived from data collected during the 2010 Census. During development, an additional attribute field was added to provide additional functionality to the users of this data. This field, named 'DEF_CAT', implies definition category, and will allow users to easily view, and create custom layers or datasets from this file. For example, new layers may created to include only incorporated cities (DEF_CAT = Incorporated), Census designated places (DEF_CAT = Incorporated OR DEF_CAT = CDP), or all cities that are neither CDP's or incorporated (DEF_CAT= Other). This data is current as of February 2012. At this time, there is no planned maintenance or update process for this dataset.This data is created to serve as base information for use in GIS systems for a variety of planning, reference, and analysis purposes. This data does not represent a legal record.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools, and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC), and with the membership, staff tracks collaborative efforts to recruit business prospects and locations. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities, and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe. This dataset provides the target and actual job creation numbers for the City of Tempe and the Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population. This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created. Additional Information Source: Extracted from GPEC monthly and annual reports and proprietary excel filesContact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary Excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

Parcel boundary lines in this dataset are published once a year, after the boundary adjustments have been approved by Planning and Zoning and certified through the Assessor's Office. Attribute data is published at different times throughout the year, as detailed below.

*Attribute data excludes ownership and address data in this dataset. If you wish to have these data, please fill out the Public Information request form found in the Download Datasets page of the GIS Portal and email to lfrederick@co.valley.id.us.

ATTRIBUTE DATA - MONTHLY UPDATES

These fields are updated in the dataset monthly. After the public table updates are run by the Assessor's Office, Valley County GIS analyst exports the tables to append/update the new data values.

ATTRIBUTE DATA - ANNUAL UPDATES

These fields are updated annually after certification of parcel boundaries and valuation have been completed.

Xtract.io’s massive 3.5M+ POI database represents a transformative resource for advanced location intelligence across the United States and Canada. Data scientists, GIS professionals, big data analysts, market researchers, and strategic planners can leverage these comprehensive places data insights to develop sophisticated market strategies, conduct advanced spatial analyses, and gain a deep understanding of regional geographical landscapes.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape with comprehensive POI coverage.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive POI database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Comprehensive Location Data? At LocationsXYZ, we: -Deliver 3.5M+ POI data with 95% accuracy -Refresh places data every 30, 60, or 90 days to ensure the most recent information -Create on-demand comprehensive POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide multi-industry POI data and polygon data in multiple file formats

Unlock the Power of Places Data With our comprehensive location intelligence, you can: -Perform thorough market analyses across multiple industries -Identify the best locations for new stores using POI database insights -Gain insights into consumer behavior with places data -Achieve an edge with competitive intelligence using comprehensive coverage

LocationsXYZ has empowered businesses with geospatial insights and comprehensive location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge 3.5M+ POI database.

Description:

Costs to the City for salary and benefits are listed by job title. Costs to the City for salary and benefits are listed by job title. Data contains historical information from the year 2014 to the Present

Acknowledgements & Source:

Dataset authored and provided by: City of Bellevue

Area Covered: Bellevue

Data Source

View Details

image source

In Geographic Information Systems (GIS), geoprocessing workflows allow analysts to organize their methods on spatial data in complex chains. We propose a method for expressing workflows as linked data, and for semi-automatically enriching them with semantics on the level of their operations and datasets. Linked workflows can be easily published on the Web and queried for types of inputs, results, or tools. Thus, GIS analysts can reuse their workflows in a modular way, selecting, adapting, and recommending resources based on compatible semantic types. Our typing approach starts from minimal annotations of workflow operations with classes of GIS tools, and then propagates data types and implicit semantic structures through the workflow using an OWL typing scheme and SPARQL rules by backtracking over GIS operations. The method is implemented in Python and is evaluated on two real-world geoprocessing workflows, generated with Esri's ArcGIS. To illustrate the potential applications of our typing method, we formulate and execute competency questions over these workflows.

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneAnalysis: Optimized for analysis What can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location. This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme. Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See the Living Atlas Imagery Layers Optimized for Analysis Group for a complete list of imagery layers optimized for analysis. Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

Dataset featuring the full-time, part-time and seasonal jobs, as well as internships posted on the City's job portal @ https://www.raleighnc.gov/jobs This dataset is updated weekdays by 9am and does not contain past (non-active) postings.

Levee stations, usually in feet but in some cases miles, snapped to 2017 Delta levee centerlines (derived from the 2017 Delta LiDAR). Base source for station locations are surveyed field markers on the levees or distance-derived CAD files, in either case as supplied by local maintaining agency's engineers. DWR collected station location data and snapped the stations into the levee centerline file from 2012. After updated levee centerlines were created, the existing points were snapped to the new lines. So there is some small difference between the supplied station locations, previous station locations and these station locations. In some cases, multiple series of stations exist for a district, generally associated with distinct waterways. Also, district levees may be demarked in feet or in miles. The label fields are simply cartographic support, the label data are identical in all cases, but are provided to support fast labeling at more infrequent intervals as needed. Stationing is not as simple as it may seem. In some cases, multiple sets of stationing exist for a district's levees (see Sherman Island for example). What this dataset intends to represent is the current stationing used by District engineers for that District on levee maintenance and improvement projects. As changes are made to the stationing, and the new stationing data become available to the Levee Program, they will be added to this database. Some islands also have separate groups of stations for various parts of the district. This version is current as of 03/24/2020. Source of the original levee stationing is DWR Delta Levees Program, compiled from data provided by internal files, from CSU Chico State, MBK Engineers, KSN Engineers, Siegfried Engineers, Malani & Associates, Green Mountain Engineers, and DCC Engineers. Processing work done by CA DWR, Division of Engineering, Geodetic Branch, Geospatial Data Support Section, specifically by Arina Ushakova (Research Data Analyst I), and initial QC by Joel Dudas (Senior Engineer, Water Resources).

This dataset maps Public Safety Answering Points (PSAPs) throughout the Mid South Region. A PSAP is a dedicated call center that receives 9-1-1 calls for police, fire, or emergency medical services. These facilities operate continuously — 24 hours a day, 7 days a week — ensuring uninterrupted emergency coverage.How It WorksWhen a 9-1-1 call is placed, trained telecommunicators at the PSAP either:Dispatch appropriate emergency responders directly, orTransfer the call to another public or private safety agency when specialized response is needed. Dataset ContentsThe feature layer includes:PSAP name and facility informationAgency affiliation and service jurisdictionAttributes supporting coverage analysis and interoperability Use CasesThis dataset supports:Operational awareness – visualizing where PSAPs are located and how jurisdictions are servedInteragency coordination – enabling effective response during large-scale incidents and disastersNext Generation 9-1-1 (NG9-1-1) – preparing for accurate, location-based routing of callsStrategic planning – identifying service overlaps, coverage gaps, and opportunities for system improvement Why It MattersPSAPs are the backbone of emergency communications. By centralizing and mapping this data, public safety leaders, GIS analysts, and emergency planners can strengthen regional readiness, improve response times, and ensure reliable access to life-saving services across the Mid South.This integrates the i3 Forest Guide principles:Forest canopy (overview) → What is this dataset about?Tree trunks (orientation) → How does it work?Branches (details) → What’s in it?Undergrowth (purpose) → Why does it matter?

This layer (hosted feature layer) depicts the citizens that are water customers in the City of Canton, GA. This data set is maintained by the City of Canton's GIS division, and is updated on a regular basis to depict the current customers. For specific questions about this data or to provide feedback, please contact the City's GIS division: Alaina Ellis GIS Analyst alaina.ellis@cantonga.gov (770) 546-6780 Canton City Hall 110 Academy Street, Canton, GA 30114

This layer considers jobs and destinations to be accessible by bike if the destinations are reachable within 30 minutes. Access to jobs and destinations within a fixed time is measured using the actual networks and not a straight line distance. Destinations include grocery stores, hospitals, community services, education centers, and other significant community areas. Jobs across the region (not just within the District) were used to provide a full picture of employment access.

Initial attack frequency zones are used by pilots and dispatchers for purposes of response to incidents such as wildland fires. Initial attack frequency zones are agreed upon annually by the Communications Duty Officer at the National Interagency Fire Center (NIFC), other frequency managers, and the FAA, and can't be changed during the year without required approval from the CDO at NIFC. Each zone has assigned to it FAA-issued frequencies that are to be used only within the zone boundary. The initial attack frequency zones are delineated to help ensure that frequencies used do not "bleed" over into other incident areas and causing issues for incident communications. The data contains no actual frequencies, but does contain the zones in which they are used. 01/12/2023 - Tabular changes only. Oregon Initial Attack Frequency Zones renumbered per Kim Albracht, Communications Duty Officer, with input from other Northwest personnel. Edits by JKuenzi, USFS. Changes are as follows:OR09 changed to OR02OR02 changed to OR03OR03 changed to OR04OR04 changed to OR05OR05 changed to OR07OR07 changed to OR08OR08 changed to OR09OR01 and OR06 remained unchanged.01/10/2023 - Geospatial and tabular changes made. Two islands on west side of OR05 absorbed into OR03. Change made to both Initial Attack Frequency Zones-Federal and to Dispatch Boundaries per Kaleigh Johnson (Asst Ctr Mgr), Jada Altman (Dispatch Ctr Mgr), and Jerry Messinger (Air Tactical Group Supervisor). Edits by JKuenzi, USFS. 01/09/2023 - Geospatial and tabular changes to align Federal Frequency Zones to Dispatch Area boundaries in Northwest GACC. No alignments made to USWACAC, USWAYAC, or USORWSC. Changes approved by Ted Pierce (NW Deputy Coordination Ctr Mgr), Kaleigh Johnson (Assistant Ctr Mgr), and Kim Albracht (Communications Duty Officer). Edits by JKuenzi, USFS. Specific changes include: WA02 changed to WA04. New WA02 carved out of WA01 and OR01. OR09 carved out of OR01 and OR02. Boundary adjustments between OR07, OR05, and OR03.11/8/2022 - Geospatial and tabular changes. Boundary modified between Big Horn and Rosebud Counties of MT07 and MT08 per KSorenson and KPluhar. Edits by JKuenzi, USFS. 09/06/2022-09/26/2022 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southern California and Great Basin in the state of Nevada. Boundary modified between CA03 and NV03, specifically between Queen Valley and Mono Valley. The team making the changes is made up of Southern Calif (JTomaselli) and Great Basin (GDingman) GACCs, with input from Ian Mills and Lance Rosen (BLM). Changes proposed will be put into effect for the 2023 calendar year, and will also impact alignments of GACC boundaries and Dispatch boundaries in the area described. Initial edits provided by Ian Mills and Daniel Yarborough. Final edits by JKuenzi, USFS. A description of the change is as follows: The northwest end of changes start approximately 1 mile west of Mt Olsen and approximately 0.5 mile south of the Virginia Lakes area.Head northwest passing on the northeast side of Red Lake and the south side of Big Virginia Lake to follow HWY 395 North east to CA 270.East through Bodie to the CA/NV state line.Follows the CA/NV State Line south to HWY CA 167/NV 359.East on NV359 to where the HWY intersects the corner of FS/BLM land.Follows the FS/BLM boundary to the east and then south where it ties into the current GACC boundary. 09/07/2022 - 09/08/2022 - Tabular and geospatial changes. Multiple boundaries modified in Northern Rockies GACC to bring Dispatch Boundaries and Initial Attack Frequency Zone lines closer in accordance with State boundaries. Information provided by Don Copple, State Fire Planning & Intelligence Program Manager for Montana Dept of Natural Resources & Conservation (DNRC), Kathy Pipkin, Northern Rockies GACC Center Manager, and Kat Sorenson, R1 Asst Aircraft Coordinator. Edits by JKuenzi, USFS. The following changes were made:Initial Attack Frequency Zone changes made to the following: Dillon Interagency Dispatch Ctr (USMTDDC) (MT03), Helena Interagency Dispatch Ctr (USMTHDC) (MT04), Lewistown Interagency Dispatch Ctr (USMTLEC) (MT06), and Missoula Interagency Dispatch Ctr (USMTMDC) (MT02).Talk was also directed to removing the Initial Attack Frequency Zone line between MT05 and MT07, but that currently remains unchanged until Telecommunications (Kimberly Albracht) can get approval from the Frequency Managers and the FAA.10/15/2021 - Geospatial and tabular changes. Boundary alignments for the Duck Valley Reservation in southern Idaho along the Nevada border. Changes impacting ID02 and NV01. The Duck Valley Reservation remains within NV01. The only change was to the alignment of the physical boundary surrounding the Reservation in accordance with the boundary shown on the 7.5 minute quadrangle maps and data supplied by CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. 9/30/2021 - Geospatial and tabular changes. Boundary alignments for Idaho on Hwy 95 NE of Weiser between Boise Dispatch Center and Payette Interagency Dispatch Center - per CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: Weiser (T11N R5W Sec 32), (T11N, R5W, Sec 3), (T12N R5W, Sec 25), and Midvale.9/21/2021 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southwestern and Southern GACCs where a portion of Texas, formerly under Southwestern GACC direction was moved to the Southern GACC. Changes to Federal Initial Attack Frequency Zones by Kim Albracht, Communications Duty Officer (CDO) include the following: State designation TXS06 changed to federal TX06.State designation TXS05 changed to federal TX05.State designation TXS04 changed to federal TX04.State designation TXS03 changed to federal TX03.State designation TXS02 changed to federal TX02.State designation TXS01 changed to federal TX01.The Oklahoma Panhandle, formerly TXS01 changed to OK04.All changes proposed for implementation starting in January 2022. Edits by JKuenzi, USFS. See also data sets for Geographic Area Coordination Centers (GACC), and Dispatch Boundary for related changes.8/17/2021 - Tabular changes only. As part of GACC realignment for 2022, area changed from state designation TXS01 to federal TX01 per Kim Albracht, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC). Edits by JKuenzi, USFS. 2/19/2021 - Geospatial and tabular changes. Boundary changes for Idaho originally submitted in 2016 but never completed in entirety. Changes between Initial Attack Zones ID01 and ID02 and with Dispatch Boundaries - per Chris Clay-BLM Boise, DeniseTolness-DOI/BLM ID State Office GIS Specialist, and Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: (T13N R3E Sec 25), (T15N R3E Sec 31), (T16N R3E Sec 18-20, and 30), and (T16N R2E Sec 13) all from ID02 to ID01. (T10N R4E Sec 4-9,17-18, 20) and (T11N R4E Sec15-16, 21-22, 27-29, 34-31) from ID01 to ID02. 11/10/2020 - Michigan split from MI01 only, to MI01(Upper Penninsula) and MI02 in the south, per Kim Albracht, Communications Duty Officer. No change made to Dispatch Zone Boundary. Edits by JKuenzi. 11/4/2020 - Oregon OR07 divided into OR07 and OR08 per Kim Albracht, Communications Duty Officer. Edits by JKuenzi.10/26/2020 - Multiple boundary changes made to Federal Initial Attack Zones, but without any change to Dispatch Zone Boundaries: Raft River District of Sawtooth National Forest changed from UT01 to ID04; land east of Black Pine District of Sawtooth National Forest changed from ID05 to ID04. Direction from Denise Tolness, DOI/BLM GIS Specialist, and Gina Dingman, Great Basin Coordination Center Manager. Parts of Craters of the Moon National Monument changed from ID04 to ID05; Sheep Mountain (Red Rocks) area changed from MT03 to ID05, per Denise Tolness, Gina Dingman, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits for all changes made by JKuenzi.4/2/2020 - State owned land added and a portion of the boundary modified between MT01 and MT02 per Mike J Gibbons, Flathead Dispatch Center Mgr, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits by JKuenzi.2/21/2020 - Existing boundaries are updated, where possible, to a uniform base layer using the August 2019 Census State & County boundaries, along with Geographic Area Command Center boundaries, Dispatch Zone Boundaries, and Initial Attack State Zones. Edits by JKuenzi.2019-2020 - Initial Attack Frequency Zone data was provided by Kim Albracht, Acting and Permanent Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID. Efforts made to tie changes with the Initial Attack Frequency Zones to other closely related datasets such as Geospatial Area Command Centers (GACCs),and Dispatch Areas, Major work completed to bring all the datasets up to date on consistent base data (8/2019 Census data), into alignment where possible, and to establish a scheduled update cycle for the nation. 2017-2019 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID.2015-2016 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Dianna Sampson, BLM Geospatial Data Analyst, NIFC, Boise, ID.

Analysis of ‘AE/VCE Confirmed Vernal Pools’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/86c3c30c-b5f3-4a22-841b-6f685fbf0fa5 on 20 November 2021.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

These products were developed to provide scientific and correspondingly spatially explicit information regarding the distribution and abundance of conifers (namely, singleleaf pinyon (Pinus monophylla), Utah juniper (Juniperus osteosperma), and western juniper (Juniperus occidentalis)) in Nevada and portions of northeastern California. Encroachment of these trees into sagebrush ecosystems of the Great Basin can present a threat to populations of greater sage-grouse (Centrocercus urophasianus). These data provide land managers and other interested parties with a high-resolution representation of conifers across the range of sage-grouse habitat in Nevada and northeastern California that can be used for a variety of management and research applications. We mapped conifer trees at 1 x 1 meter resolution across the extent of all Nevada Department of Wildlife Sage-grouse Population Management Units plus a 10 km buffer. Using 2010 and 2013 National Agriculture Imagery Program digital orthophoto quads (DOQQs) as our reference imagery, we applied object-based image analysis with Feature Analyst software (Overwatch, 2013) to classify conifer features across our study extent. This method relies on machine learning algorithms that extract features from imagery based on their spectral and spatial signatures. Conifers in 6230 DOQQs were classified and outputs were then tested for errors of omission and commission using stratified random sampling. Results of the random sampling were used to populate a confusion matrix and calculate the overall map accuracy of 84.3 percent. We provide 5 sets of products for this mapping process across the entire mapping extent: (1) a shapefile representing accuracy results linked to our mapping subunits; (2) binary rasters representing conifer presence or absence at a 1 x 1 meter resolution; (3) a 30 x 30 meter resolution raster representing percentage of conifer canopy cover within each cell from 0 to 100; (4) 1 x 1 meter resolution canopy cover classification rasters derived from a 50 meter radius moving window analysis; and (5) a raster prioritizing pinyon-juniper management for sage-grouse habitat restoration efforts. The latter three products can be reclassified into user-specified bins to meet different management or study objectives, which include approximations for phases of encroachment. These products complement, and in some cases improve upon, existing conifer maps in the western United States, and will help facilitate sage-grouse habitat management and sagebrush ecosystem restoration. These data support the following publication: Coates, P.S., Gustafson, K.B., Roth, C.L., Chenaille, M.P., Ricca, M.A., Mauch, Kimberly, Sanchez-Chopitea, Erika, Kroger, T.J., Perry, W.M., and Casazza, M.L., 2017, Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales: U.S. Geological Survey Open-File Report 2017-1093, 40 p., https://doi.org/10.3133/ofr20171093. References: ESRI, 2013, ArcGIS Desktop: Release 10.2: Environmental Systems Research Institute. Overwatch, 2013, Feature Analyst Version 5.1.2.0 for ArcGIS: Overwatch Systems Ltd.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5\tSurface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

Mapping of Visible Surface Water (VSW), or water features not concealed by other objects (i.e., tree canopy, bridges, etc.), is an important component of landcover models. VSW is not intended to represent a full hydrography or show connectivity, like other available water datasets – like NHD – whose boundaries may include other landcover types (i.e., shrubs, trees, etc.). Each feature has been visually verified and given attributes by an analyst. This dataset is also unique in that it reflects surface water for a single year - 2017. A variety of funding sources acquired between 2019 and 2023 aided the completion of the dataset for the entire state of Washington. More information on the dataset, current data coverage, and applications can be found on our website: https://hrcd-wdfw.hub.arcgis.com/.

Tip: Try using the filter options on the data tab to limit your download to a single County or WRIA. The filtered download can take a substantial amount of time to initiate, so it may be necessary to download the full dataset if the filter option does not work.

PurposeThis map is designed to be loaded into ArcGIS Pro projects to give users easy access to sanitary sewer utility data.BackgroundPrior to the creation of this map, non-GIS users would only be able to access static copies of utility data in the ArcGIS Online utility web maps/apps. This basemap allows power users to access a live feed of the data, which is updated on a nightly basis. This map can be opened in ArcGIS Pro to give users more flexibility to analyze and explore the data. The map and the data within are for internal use only.ProcessThe GIS Analyst updates this dataset on a continuous basis using As-Builts and CAD files submitted during the development review process, as well as field staff verification

This dataset contains 10,000 synthetic records simulating the migratory behavior of various bird species across global regions. Each entry represents a single bird tagged with a tracking device and includes detailed information such as flight distance, speed, altitude, weather conditions, tagging information, and migration outcomes.

The data was entirely synthetically generated using randomized yet realistic values based on known ranges from ornithological studies. It is ideal for practicing data analysis and visualization techniques without privacy concerns or real-world data access restrictions. Because it’s artificial, the dataset can be freely used in education, portfolio projects, demo dashboards, machine learning pipelines, or business intelligence training.

With over 40 columns, this dataset supports a wide array of analysis types. Analysts can explore questions like “Do certain species migrate in larger flocks?”, “How does weather impact nesting success?”, or “What conditions lead to migration interruptions?”. Users can also perform geospatial mapping of start and end locations, cluster birds by behavior, or build time series models based on migration months and environmental factors.

For data visualization, tools like Power BI, Python (Matplotlib/Seaborn/Plotly), or Excel can be used to create insightful dashboards and interactive charts.

Join the Fabric Community DataViz Contest | May 2025: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/%EF%B8%8F-Fabric-Community-DataViz-Contest-May-2025/ba-p/4668560

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.