Analysis of ‘2018 CT Data Catalog (Non GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fe457197-5afe-4a20-a131-1bdcf9bd8ace on 26 January 2022.

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

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog does not contain information about high value GIS data, which is compiled in a separate data inventory at the following link: https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 3/4/2019 and will continue to be updated as high value data inventories are submitted to OPM.

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

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset.

Toolbox Use
License
Creative Commons-PDDC
Recommended Citation
Welty JL, Jeffries MI, Arkle RS, Pilliod DS, Kemp SK. 2021. GIS Clipping and Summarization Toolbox: U.S. Geological Survey Software Release. https://doi.org/10.5066/P99X8558

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.

The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.” Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety element of the evacuation routes from different jurisdictions as well as their use in various types of evacuation scenarios such as wildfire, flooding, or landslides. 2) Perform network analyses and modeling based on the team’s experience with road network performance, access restoration, and critical infrastructure modeling, for a set of case studies, as well as, assessing their performance considering the latest evacuation research. 3) Analyze how well current bus and rail routes align with evacuation routes; and for a series of case studies, using data from previous evacuations, evaluate how well aligned the safety elements of the emerging plans are, relative to previous evacuation routes. And 4) analyze different metrics about the performance of the evacuation routes for different segments of the population (e.g., elderly, mobility constrained, non-vehicle households, and disadvantaged communities). The database and assessments will help inform infrastructure investment decisions and to develop recommendations on how best to maintain State transportation assets and secure safe evacuation routes, as they will identify the road segments with the largest impact on the evacuation route/network performance. The project will deliver a GIS of the compiled plans, a report summarizing the creation of the database and the analyses and will make a final presentation of the study results. Methods The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk.

Analysis of ‘PLACES: Census Tract Data (GIS Friendly Format), 2020 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/36454ff3-3bd6-4626-8607-ed62ff3f4619 on 12 February 2022.

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

This dataset contains model-based census tract level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 27 measures at the census tract level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

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

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

The USDA-ARS Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona as an outdoor laboratory for studying semiarid rangeland hydrologic, ecosystem, climate, and erosion processes. Since its establishment in 1953, the SWRC in Tucson, Arizona, has collected, processed, managed, and disseminated high-resolution, spatially distributed hydrologic data in support of the center’s mission. Data management at the SWRC has evolved through time in response to new computing, storage, and data access technologies. In 1996, the SWRC initiated a multiyear project to upgrade rainfall and runoff sensors and convert analog systems to digital electronic systems supported by data loggers. This conversion was coupled with radio telemetry to remotely transmit recorded data to a central computer, thus greatly reducing operational overhead by reducing labor, maintenance, and data processing time. A concurrent effort was initiated to improve access to SWRC data by creating a system based on a relational database supporting access to the data via the Internet. An SWRC team made up of scientists, IT specialists, programmers, hydrologic technicians, and instrumentation specialists was formed. This effort is termed the Southwest Watershed Research Center Data Access Project (DAP). The goal of the SWRC DAP is to efficiently disseminate data to researchers; land owners, users, and managers; and to the public. Primary access to the data is provided through a Web-based user interface. In addition, data can be accessed directly from within the SWRC network. The first priority for the DAP was to assimilate and make available rainfall and runoff data collected from two instrumented field sites, the WGEW near Tombstone, Arizona, and the Santa Rita Experimental Range (SRER) south of Tucson, Arizona. This web map describes the associated GIS layers. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/fe4ac74f13484a169899b166159e0bb5

Analysis of ‘2019 CT Data Catalog (GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/168eaac6-5f52-4015-be99-93031db2fd0d on 26 January 2022.

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

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management, updated in 2019. This catalog contains information on high value GIS data only. A catalog of high value non-GIS data may be found at the following link: https://data.ct.gov/Government/2019-CT-Data-Catalog-Non-GIS-/f6rf-n3ke

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 2/3/2020 and will continue to be updated as high value data inventories are submitted to OPM.

The 2018 high value data inventories for Non-GIS and GIS data can be found at the following links: CT Data Catalog (Non GIS): https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn/ CT Data Catalog (GIS): https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27 Less

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

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

This dataset consists of high-resolution orthographic imagery collected in 2024 to support forestry management, land use planning, and ecological assessment in Mountain Village. The imagery was acquired using drone-based aerial surveys, providing detailed, georeferenced imagery for various applications, including: Vegetation and forest health monitoring Wildfire risk assessment and mitigation planning Land use and zoning analysis Topographic and hydrological mapping The dataset is designed for use by GIS analysts, planners, environmental researchers, and forestry professionals to support accurate spatial analysis and decision-making.Technical Specifications:

Acquisition Year: 2024Imagery Type: Orthorectified aerial imageryResolution: 1-meterCoordinate System: NAD 1983 (CORS96) SPCS Colorado SouthFormat: GeoTIFF

Using the coronavirus infographic template in Business/Community Analyst Web (ArcGIS Blog).Business Analyst (BA) Web infographics are a powerful way to understand demographics and other information in context. This blog article explains how your organization can use the Coronavirus infographic template that was added to the infographics gallery on March 1, 2020._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

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 Surface 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

Xverum’s Point of Interest (POI) Data is a comprehensive dataset of 230M+ verified locations, covering businesses, commercial properties, and public places across 5000+ industry categories. Our dataset enables retailers, investors, and GIS professionals to make data-driven decisions for business expansion, location intelligence, and geographic analysis.

With regular updates and continuous POI discovery, Xverum ensures your mapping and business location models have the latest data on business openings, closures, and geographic trends. Delivered in bulk via S3 Bucket or cloud storage, our dataset integrates seamlessly into geospatial analysis, market research, and navigation platforms.

🔥 Key Features:

📌 Comprehensive POI Coverage ✅ 230M+ global business & location data points, spanning 5000+ industry categories. ✅ Covers retail stores, corporate offices, hospitality venues, service providers & public spaces.

🌍 Geographic & Business Location Insights ✅ Latitude & longitude coordinates for accurate mapping & navigation. ✅ Country, state, city, and postal code classifications. ✅ Business status tracking – Open, temporarily closed, permanently closed.

🆕 Continuous Discovery & Regular Updates ✅ New business locations & POIs added continuously. ✅ Regular updates to reflect business openings, closures & relocations.

📊 Rich Business & Location Data ✅ Company name, industry classification & category insights. ✅ Contact details, including phone number & website (if available). ✅ Consumer review insights, including rating distribution (optional feature).

📍 Optimized for Business & Geographic Analysis ✅ Supports GIS, navigation systems & real estate site selection. ✅ Enhances location-based marketing & competitive analysis. ✅ Enables data-driven decision-making for business expansion & urban planning.

🔐 Bulk Data Delivery (NO API) ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured formats (.csv, .json, .xml) for seamless integration.

🏆 Primary Use Cases:

📈 Business Expansion & Market Research 🔹 Identify key business locations & competitors for strategic growth. 🔹 Assess market saturation & regional industry presence.

📊 Geographic Intelligence & Mapping Solutions 🔹 Enhance GIS platforms & navigation systems with precise POI data. 🔹 Support smart city & infrastructure planning with location insights.

🏪 Retail Site Selection & Consumer Insights 🔹 Analyze high-traffic locations for new store placements. 🔹 Understand customer behavior through business density & POI patterns.

🌍 Location-Based Advertising & Geospatial Analytics 🔹 Improve targeted marketing with location-based insights. 🔹 Leverage geographic data for precision advertising & customer segmentation.

💡 Why Choose Xverum’s POI Data? - 230M+ Verified POI Records – One of the largest & most structured business location datasets available. - Global Coverage – Spanning 249+ countries, covering all major business categories. - Regular Updates & New POI Discoveries – Ensuring accuracy. - Comprehensive Geographic & Business Data – Coordinates, industry classifications & category insights. - Bulk Dataset Delivery (NO API) – Direct access via S3 Bucket or cloud storage. - 100% GDPR & CCPA-Compliant – Ethically sourced & legally compliant.

Access Xverum’s 230M+ POI Data for business location intelligence, geographic analysis & market research. Request a free sample or contact us to customize your dataset today!

The Southwestern Region is 20.6 million acres. There are six national forests in Arizona, five national forests and a national grassland in New Mexico, and one national grassland each in Oklahoma and the Texas panhandle.The region ranges in elevation from 1,600 feet above sea level and an annual rain fall of 8 inches in Arizona's lower Sonoran Desert to 13,171-foot high Wheeler Peak and over 35 inches of precipitation a year in northern New Mexico. Geographic Information Systems or GIS are computer systems, software and data used to analyze and display spatial or locational data about surface features. One of the strengths of GIS is the capability to overlay or compare multiple feature layers. A user can then analyze the relationship between the layers. Data, reports and maps produced through GIS are used by managers and resource specialists to make decisions about land management activities on National Forests. The National Forests of the Southwestern Region maintain and utilize GIS data for various features on the ground. Some of these datasets are made available for download through this page. Resources in this dataset:Resource Title: GIS Datasets. File Name: Web Page, url: https://www.fs.usda.gov/detail/r3/landmanagement/gis/?cid=STELPRDB5202474 Selected GIS datasets for the Southwestern Region are available for download from this page.Resource Software Recommended: ArcExplorer,url: http://www.esri.com/software/arcexplorer/index.html

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq This dataset was created to depict approximate tree canopy cover for all land within the City of Austin's "full watershed regulation area." Intended for planning purposes and measuring citywide percent canopy. Definition: Tree canopy is defined as the layer of leaves, branches, and stems of trees that cover the ground when viewed from above. Methods: The 2022 tree canopy layer was derived from satellite imagery (Maxar) and aerial imagery (NAIP). Images were used to extract tree canopy into GIS vector features. First, a “visual recognition engine” generated the vector features. The engine used machine learning algorithms to detect and label image pixels as tree canopy. Then using prior knowledge of feature geometries, more modeling algorithms were used to predict and transform probability maps of labeled pixels into finished vector polygons depicting tree canopy. The resulting features were reviewed and edited through manual interpretation by GIS professionals. When appropriate, NAIP 2022 aerial imagery supplemented satellite images that had cloud cover, and a manual editing process made sure tree canopy represented 2022 conditions. Finally, an independent accuracy assessment was performed by the City of Austin and the Texas A&M Forest Service for quality assurance. GIS professionals assessed agreement between the tree canopy data and its source satellite imagery. An overall accuracy of 98% was found. Only 23 errors were found out of a total 1,000 locations reviewed. These were mostly omission errors (e.g. not including canopy in this dataset when canopy is shown in the satellite or aerial image). Best efforts were made to ensure ground-truth locations contained a tree on the ground. To ensure this, location data were used from City of Austin and Texas A&M Forest Service databases. Analysis: The City of Austin measures tree canopy using the calculation: acres of tree canopy divided by acres of land. The area of interest for the land acres is evaluated at the City of Austin's jurisdiction including Full Purpose, Limited Purpose, and Extraterritorial jurisdictions as of May 2023. New data show, in 2022, tree canopy covered 41% of the total land area within Austin's city limits (using city limit boundaries May 2023 and included in the download as layer name "city_of_austin_2023"). 160,046.50 canopy acres (2022) / 395,037.53 land acres = 40.51% ~41%. This compares to 36% last measured in 2018, and a historical average that’s also hovered around 36%. The time period between 2018 and 2022 saw a 5 percentage point change resulting in over 19K acres of canopy gained (estimated). Data Disclaimer: It's possible changes in percent canopy over the years is due to annexation and improved data methods (e.g. higher resolution imagery, AI, software used, etc.) in addition to actual in changes in tree canopy cover on the ground. For planning purposes only. Dataset does not account for individual trees, tree species nor any metric for tree canopy height. Tree canopy data is provided in vector GIS format housed in a Geodatabase. Download and unzip the folder to get started. Please note, errors may exist in this dataset due to the variation in species composition and land use found across the study area. This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by the City of Austin for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness. Data Provider: Ecopia AI Tech Corporation and PlanIT Geo, Inc. Data derived from Maxar Technologies, Inc. and USDA NAIP imagery

This service is NOT for download but may be viewed in web maps and apps.Georeferenced Bedrock Geology of Wisconsin, West-Central Sheet, 1988. The Bedrock Geology of Wisconsin, West-Central Sheet was developed to provide a reliable, accurate and detailed representation of the state’s subsurface geology. It supports a variety of applications, including geological research, land-use planning, resource management, environmental conservation, education, public outreach, and policy development. By offering insights into the composition, structure, and distribution of bedrock formations, the map aids in identifying potential geological hazards and areas of scientific or ecological significance and helps planners, researchers, and decision-makers make informed choices.

The statewide geologic map symbol for each formation is standardized for consistency and can be found in the accompanying legend. The legend provides essential information, including formation names, lithologic descriptions, geologic age, and the symbology used in the dataset. This map and its data were developed by UW Extension-Geologic and Natural History Survey (WGNHS), B.A. Brown Visit WGNHS to search for WGNHS maps and contact WGNHS at info@wgnhs.wisc.edu with any questions about this map or data. The map was georeferenced for use in this service by the Wisconsin DNR Bureau of Drinking Water and Groundwater, Water Use Section. For any questions contact the Bureau of Drinking Water and Groundwater GIS Analyst at DNRDGGISAPPS@wisconsin.gov. This data was mapped at a small scale (1:250,000), making it unsuitable for detailed local analysis or site-specific decision-making. Users are advised to consult local or higher-resolution datasets when conducting detailed analyses or making critical decisions.

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.

The National Weather Service (NWS) Storm Prediction Center (SPC) routinely collects reports of severe weather and compiles them with public access from the database called SeverePlot (Hart and Janish 1999) with a Graphic Information System (GIS). The composite SVRGIS information is made available to the public primarily in .zip files of approximately 50MB size. The files located at the access point have organized severe weather data by County Warning Area (CWA). A CWA is a grouping of counties for which severe weather information is distributed. Although available to all, the data provided may be of particular value to weather professionals and students of meteorological sciences. An instructional manual is provided on how to build and develop a basic severe weather report GIS database in ArcGis and is located at the technical documentation site contained in this metadata catalog.

This service is NOT for download but may be viewed in web maps and apps.Georeferenced Statewide Bedrock Geologic Map of Wisconsin, 1982. The Statewide Bedrock Geologic Map of Wisconsin was developed to provide a reliable, accurate and detailed representation of the state’s subsurface geology. It supports a variety of applications, including geological research, land-use planning, resource management, environmental conservation, education, public outreach, and policy development. By offering insights into the composition, structure, and distribution of bedrock formations, the map aids in identifying potential geological hazards and areas of scientific or ecological significance and helps planners, researchers, and decision-makers make informed choices.

The statewide geologic map symbol for each formation is standardized for consistency and can be found in the accompanying legend. The legend provides essential information, including formation names, lithologic descriptions, geologic age, and the symbology used in the dataset. This map and its data were developed by UW Extension-Geologic and Natural History Survey (WGNHS), Meredith E. Ostrom. Visit WGNHS Maps & Publications search for WGNHS maps and contact WGNHS at info@wgnhs.wisc.edu with any questions about this map or data. The map was georeferenced for use in this service by the Wisconsin DNR Bureau of Drinking Water and Groundwater, Water Use Section. For any questions contact the Bureau of Drinking Water and Groundwater GIS Analyst at DNRDGGISAPPS@wisconsin.gov. This data was mapped at a very small scale (ranging from 1:5,000,000 to 1:500,000), making it unsuitable for detailed local analysis or site-specific decision-making. Users are advised to consult local or higher-resolution datasets when conducting detailed analyses or making critical decisions.