The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The U.S. Fish and Wildlife Service Corporate Master Table (CMT) is the official source of Service organization codes and related information. Information in the CMT includes, but is not limited to, organization codes, organization names, Federal Budget Management System (FBMS), cost center codes, fire unit identifiers, program names, mailing and physical/shipping addresses, telephone and fax numbers as well as latitude and longitude coordinates. The CMT enables all Service automated systems to utilize a corporate data set of known quality, eliminating the workload required to maintain each system's data set, and thereby facilitating data sharing. Other customers for the CMT are Service personnel who maintain directories, communicate with Congress and with the Public, maintain World Wide Web sites, etc. These spatial data were created using the information in the CMT. The CMT contains location information on all the offices within the Service that have an organization code. Unstaffed offices and some other facilities may not be included. The latitude and longitude points used are usually the location of the main administrative site. The latitude and longitude data is not completely verified but is the best we have at this time. This data set is intended to give an overview of where USFWS has stations across the United States and Territories, including locations outside the 50 states. It is not intended to be the exact location of every USFWS office. The CMT is primarily used for accounting purposes and therefore one location in the CMT can represent many different offices. Some points are duplicates where a station, most usually an Ecological Field Office, may be associated with more than one USFWS program. This data is updated from an internal authoritative source every night at 2:30am EST.For a direct link to the official Enterprise Geospatial dataset and metadata: https://ecos.fws.gov/ServCat/Reference/Profile/60076.Dataset contact: fwsgis@fws.gov

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

This layer portrays elevation as an artistic hillshade. The map is designed to be used as a backdrop for topographical, soil, hydro, landcover or other outdoor recreational maps. It’s a default relief background in various basemaps such as Topographic, Terrain with Labels.The map is compiled from a variety of data sources from commercial, community maps and many authoritative organizations across the globe. The basemap has global coverage down to a scale of ~1:72k. In the United States, parts of Europe, Asia and Africa coverage goes down to ~1:9k. To see the coverage and sources of various datasets comprising this map layer, view the Elevation Coverage Map. Additionally, this layer uses data from Maxar’s Precision 3D Digital Terrain Models for parts of the globe.The map is based on the Multi-directional hillshade algorithm.

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000. Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.

This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:

1. Geochemistry data from the US Water Quality Portal (WQP), which compiles geochemistry data from the USGS and other federal agencies.
2. Discharge data from WoKaS, a world wide spring discharge data set (Olarinoye et al., 2020).
3. Regional karst data from selected US state agencies.

Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.

Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.

Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a

Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8

Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96

Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.

Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.

Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.

https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177

This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/

Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.

TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included.A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fireincidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access ofPublic, a Feature Status ofApproved, and an Is Visible setting ofYesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service.The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group.Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning:Please refrain from repeatedly querying the service using a relative date range.This includes using the “(not) in the last” operators in a Web Map filterand any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.

This feature class/shapefile contains locations of child day care centers for the 50 states of the USA, Washington D.C., and Puerto Rico. The dataset only includes center based child day care locations (including those located at schools and religious institutes) and does not include group, home, and family based child day cares. The SOURCEDATE is an indicator of when the source data was last acquired or was publicly available. All the data was acquired from respective states departments or their open source websites and only contains data provided by these sources. Information on the source of data for each state is available in the SOURCE field of the feature class/shapefile. The TYPE attribute is a common categorization of child day care centers for all states which categorizes every child day care into Center Based, School Based, Head Start, or Religious Facility solely based on the type of facility where the child day care center is geographically located. This update has 2608 fewer records than the previous version based on source data

Read about this volunteer-driven effort, access data and apps, and contribute your own testing site data:https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataItem details page:https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the originalCOVID-19 Testing Locations in the United States - public dataset. A backup copy also exists:https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same.This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and onfindcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form:https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset.GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy ofthis templateand emailing it to admin@giscorps.org.This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated byGISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document:https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups:https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57

National Levee DatabaseThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Army Corps of Engineers (USACE), displays levees within the United States. Per USACE, "The National Levee Database captures all known levees in the United States. It provides users with the ability to search for specific data about levees and serves as a national resource to support awareness and preparedness around flooding. The USACE is responsible for maintaining the National Levee Database and works in partnership with the Federal Emergency Management Agency (FEMA), and in close collaboration with other federal, state, and local governments and entities responsible for levees to obtain and share accurate and complete information."Leveed area in Morrisville, PennsylvaniaData downloaded: 4/24/2024Data source: NLD 2 PublicNGDAID: 161 (National Levee Database)OGC API Features Link: (National Levee Database - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: National Levee DatabaseSupport documentation: NLD Data DictionaryFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Water - Inland Theme Community. Per the Federal Geospatial Data Committee (FGDC), Water - Inland is defined as the "interior hydrologic features and characteristics, including classification, measurements, location, and extent. Includes aquifers, watersheds, wetlands, navigation, water quality, water quantity, and groundwater information."For other NGDA Content: Esri Federal Datasets

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

This map layer contains the shallowest principal aquifers of the conterminous United States, Hawaii, Puerto Rico, and the U.S. Virgin Islands, portrayed as polygons. The map layer was developed as part of the effort to produce the maps published at 1:2,500,000 in the printed series "Ground Water Atlas of the United States". The published maps contain base and cultural features not included in these data. This is a replacement for the July 1998 map layer called Principal Aquifers of the 48 Conterminous United States - https://doi.org/10.3133/70046037

Hurricane Evacuation RoutesThis feature layer, utilizing data from Homeland Infrastructure Foundation Level Data (HIFLD), displays the locations of hurricane evacuation routes in the United States. Per HIFLD, "A hurricane evacuation route is a designated route used to direct traffic inland in case of a hurricane threat. This dataset is based on supplied data from Gulf Coast and Atlantic Seaboard states. The GIS data was gathered from states willing to share such data. Three (3) states (ME, NY, and NH) indicated that they do not maintain public maps showing hurricane evacuation routes and were unable or unwilling to share GIS files depicting such routes."Houston, Texas area hurricane evacuation routesData currency: Caution should be exercised by the user of this feature layer. This data is over seventeen years old and has not been updated since creation. HIFLD is inquiring with agencies to determine whether they would be willing to be data stewards for this layer going forward. (As of January 2018)Data source: Hurricane Evacuation RoutesData modification: None

For more information: Homeland Infrastructure Foundation- Level Data Subcommittee

Support documentation: MetadataFor feedback, please contact: ArcGIScomNationalMaps@esri.comHomeland Infrastructure Foundation-Level Data (HIFLD) SubcommitteePer HIFLD, "The Homeland Infrastructure Foundation-Level Data (HIFLD) Subcommittee was established…to address improvements in collection, processing, sharing, and protection of homeland infrastructure geospatial information across multiple levels of government, and to develop a common foundation of homeland infrastructure data to be used for visualization and analysis on all classification domains."

Link to Data Dictionary : https://rochesterny.maps.arcgis.com/sharing/rest/content/items/dbc4cc3c88ca49d2b46c8e059e77bb1f/dataDataset Description:This dataset contains historical fire incident reports from the Rochester Fire Department, covering the period from January 1, 1980, to December 31, 2005. The data includes fields such as the date and time of incidents, the location of the incident (with cleaned address data), and whether there were any casualties or property damage.

Data Source:This data was sourced from a paper process and legacy mainframe system used by the Rochester Fire Department prior to 2006. More recent data (from 2006 onward) is stored in a newer system and is not yet available for public release.

Purpose of Release:This dataset is being released in response to multiple Freedom of Information Law (FOIL) requests. By making the data publicly available, the City of Rochester aims to improve transparency and reduce the administrative burden associated with individual FOIL requests.

Data Cleaning:To prepare the dataset for public release:

• Personally Identifiable Information (PII) has been removed.

• A cleaned address column has been added to support geocoding and location-based analysis, while maintaining privacy.

Key Fields:This dataset contains 165 columns of data which is outlined. The full data dictionary with attachments are included. Link to Data Dictionary : https://rochesterny.maps.arcgis.com/sharing/rest/content/items/dbc4cc3c88ca49d2b46c8e059e77bb1f/data

• RFDKEY: Unique identifier for each incident.

• RFITIME: The date the incident occurred.

• INJRYCIV: Civilian or firefighter injuries or a total for both if both occurred.

• STR_NAME_&_SUFFIX_NEW: Cleaned address (geocodable) of where the incident occurred.

Known Limitations:• No Data After 2005: This dataset covers incidents up to the end of 2005 only. Fire incidents that occurred after 2005 are stored in a different system, and the data is not yet ready for public release.

• Casualty: Casualty figures are based on reports filed at the time of the incident and may not reflect later revisions or corrections.

• Personally Identifiable Information: Personally identifiable information has been removed from this data set.

How to Use This Data:This dataset is suitable for analyzing trends in fire incidents over time, understanding the frequency and distribution of different types of fire incidents, and evaluating the response to incidents in different areas of Rochester.

• Example Questions You Can Answer:

• How many fatal fires occurred between 1980 and 2005?

• Which neighborhoods experienced the most fire incidents in the 1990s?

• What was the average property damage caused by fires in 2000?

Suggested Use Cases:

• Journalists: Reporters can use this data to identify trends and patterns in fire incidents over time and in different parts of the city.

• Researchers: Academics can analyze the data to study the long-term impacts of fire safety initiatives or the correlation between fire incidents and other urban factors.

• Public: Citizens can use the data to understand the history of fire safety in their neighborhoods and the response patterns of the Rochester Fire Department.

Important Context for Users:This dataset is historical and represents data collected from 1980 to 2005. It was extracted from a paper record and manual entry into a legacy system and reflects the standards and reporting practices of that time. While every effort has been made to clean and prepare the data, there may be some inconsistencies due to the transition from the old system.

Some records may contain unusually high or low values for casualty counts, property damage estimates, or incident types. These outliers have been retained to maintain the dataset’s integrity but should be interpreted cautiously. Users are encouraged to cross-reference outliers with additional contextual data where possible.

Users should be mindful that this dataset does not include data beyond 2005, and any analysis involving more recent trends will require data from newer systems, which is not yet available. For any conclusions drawn, users should account for these limitations and the possibility of updates in the future.Additional instructions on data usage and interpretation: RFD Historical Data - Attachments

The USGS National Hydrography Dataset (NHD) service from The National Map is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000 (or larger) scale and referred to as high resolution NHD, and the other based on 1:100,000 scale and referred to as medium resolution NHD. The NHD from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. The NHD is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain NHD data in either Esri File Geodatabase or Shapefile formats. For additional information on the NHD, go to https://www.usgs.gov/national-hydrography/national-hydrography-dataset. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata. Data Refreshed January, 2024.

Truck Driving Schools in the United States This dataset is composed of any type of Post Secondary Education facility such as: colleges, universities, technical schools, or trade schools that provide training and certification in the field of professional truck driving. This dataset does not include Administration Only locations. No entities located in American Samoa, the Northern Mariana Islands, or the Virgin Islands are included in this dataset. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 12/19/2006 and the newest record dates from 1/8/2007.