In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Geographic Information System Software Market Outlook

The global Geographic Information System (GIS) Software market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach USD 15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This impressive growth can be attributed to the increasing demand for efficient data management tools across various industries, which rely on spatial data for decision-making and strategic planning. The rapid advancements in technology, such as the integration of AI and IoT with GIS software, have further propelled the market, enabling organizations to harness the full potential of geographic data in innovative ways.

One of the primary growth drivers of the GIS Software market is the burgeoning need for urban planning and smart city initiatives worldwide. As urbanization trends escalate, cities are increasingly relying on GIS technology to manage resources more effectively, optimize transportation networks, and enhance public safety. The ability of GIS software to provide real-time data and spatial analysis is vital for city planners and administrators faced with the challenges of modern urban environments. Furthermore, the trend towards digital transformation in governmental organizations is boosting the adoption of GIS solutions, as they seek to improve operational efficiency and service delivery.

The agricultural sector is also experiencing significant transformations due to the integration of GIS software, which is another pivotal growth factor for the market. Precision agriculture, which involves the use of GIS technologies to monitor and manage farming practices, is enabling farmers to increase crop yields while reducing resource consumption. By leveraging spatial data, farmers can make informed decisions about planting, irrigation, and harvesting, ultimately leading to more sustainable agricultural practices. This trend is particularly prominent in regions where agriculture forms a substantial portion of the economy, encouraging the adoption of advanced GIS tools to maintain competitive advantage.

Another influential factor contributing to the growth of the GIS Software market is the increasing importance of environmental management and disaster response. GIS technology plays a crucial role in assessing environmental changes, managing natural resources, and planning responses to natural disasters. The ability to overlay various data sets onto geographic maps allows for better analysis and understanding of environmental phenomena, making GIS indispensable in tackling issues such as climate change and resource depletion. Moreover, governments and organizations are investing heavily in GIS tools that aid in disaster preparedness and response, ensuring timely and effective action during emergencies.

The evolution of GIS Mapping Software has been instrumental in transforming how spatial data is utilized across various sectors. These software solutions offer robust tools for visualizing, analyzing, and interpreting geographic data, enabling users to make informed decisions based on spatial insights. With the ability to integrate multiple data sources, GIS Mapping Software provides a comprehensive platform for conducting spatial analysis, which is crucial for applications ranging from urban planning to environmental management. As technology continues to advance, the capabilities of GIS Mapping Software are expanding, offering more sophisticated features such as 3D visualization and real-time data processing. These advancements are not only enhancing the utility of GIS tools but also making them more accessible to a wider range of users, thereby driving their adoption across different industries.

Regionally, North America and Europe have traditionally dominated the GIS Software market, thanks to their robust technological infrastructure and higher adoption rates of advanced technologies. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on infrastructure development, and the expanding telecommunications sector. The growing awareness and adoption of GIS solutions in countries like China and India are significant contributors to this regional growth. Furthermore, Latin America and the Middle East & Africa regions are slowly catching up, with ongoing investments in smart city projects and infrastructure development driving the demand for GIS software.

Component Analysis</h2&

The following of best practices of Software Engineering (SE) is something that provides many advantages for software companies. In this scenario SWEBOK is a guideline that supports these companies with information about the core of knowledge of SE, including a list of Best Practices (BP) to adopt. For small companies, however, some restrictions such as limited budget, short schedule, reduced number of employees, can hinder the advantages of the adoption of these practices. In this scenario, it is necessary to have useful information about which BPs have been adopted in small companies. Therefore, this paper describes the planning and execution of a quasi-systematic mapping study in order to report the adopting scenario of SWEBOK BPs in small companies during the last decade. It was possible to observe that the most prominent BP adopted is “Test application”, followed by the using of “Software Process Model” where the tests’ execution is already contemplated by. On the other hand, “Budget Limitation” and “Staff Size” were cited as motivations for avoid the adoption of BPs in small companies.

The Story Map Basic application is a simple map viewer with a minimalist user interface. Apart from the title bar, an optional legend, and a configurable search box the map fills the screen. Use this app to let your map speak for itself. Your users can click features on the map to get more information in pop-ups. The Story Map Basic application puts all the emphasis on your map, so it works best when your map has great cartography and tells a clear story.You can create a Basic story map by sharing a web map as an application from the map viewer. You can also click the 'Create a Web App' button on this page to create a story map with this application. Optionally, the application source code can be downloaded for further customization and hosted on your own web server.For more information about the Story Map Basic application, a step-by-step tutorial, and a gallery of examples, please see this page on the Esri Story Maps website.

The Ministry of Natural Resources and Forestry’s Make a Topographic Map is a mapping application that features the best available topographic data and imagery for Ontario. You can: * easily toggle between traditional map backgrounds and high-resolution imagery * choose to overlay the topographic information with the imagery * turn satellite imagery on or off * customize your map by adding your own text * print your custom map Data features include: * roads * trails * lakes * rivers * wooded areas * wetlands * provincial parks * municipal, township and other administrative boundaries You don’t need special software or licenses to use this application. Technical information Using cached imagery and topographic data, the application provides a fast, seamless display at pre-defined scales. The caches are updated annually.

ADOT has 27 interactive maps, dashboards, PDF reports, and instructional materials available to our customers, including ADOT staff, State Representatives, local and tribal government agencies, private agencies, and the public. Even with recent efforts to reorganize our ADOT Maps website, it has been difficult for our customers to find the product that has the information they need.

This experience builder app includes links to all of our products and includes filters that help people locate the product that would be the most useful to them. The backend of this app is a single table with information and links to each product.This app is now available on the newly redesigned ADOT Maps website (https://azdot.gov/maps).

5-Digit and 3-Digit ZIP Code data for Maptitude mapping software are from Caliper Corporation and contain boundaries and demographic data.

Chapter 6: sUAS for Wildlife Conservation – Assessing Habitat Quality of the Endangered Black-Footed Ferret Donna M. Delparte, Kristy Bly, Travis Stone, Sarah Olimb, Michael Kinsey, Matthew Belt, Thomas Calton Advances in high spatial resolution mapping capabilities and the new rules established by the Federal Aviation Administration in the United States for the operation of Small Unmanned Aircraft Systems (sUAS) have provided new opportunities to acquire aerial data at a lower cost and more safely versus other methods. A similar opening of the skies for sUAS applications is being allowed in countries across the world. Also, sUAS can access hazardous or inaccessible areas during disaster events and provide rapid response when needed. Applications of Small Unmanned Aircraft systems: Best Practices and Case Studies is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. Very few sUAS pilots have the knowledge of how the collected imagery is processed into value added mapping products that have commercial and/or academic import. Since the field of sUAS applications is just a few years old, this book covers the need for a compendium of case studies to guide the planning, data collection, and most importantly data processing and map error issues, with the range of sensors available to the user community. Written by experienced academics and professionals, this book serves as a guide on how to formulate sUAS based projects, from choice of a sUAS, flight planning for a particular application, sensors and data acquisition, data processing software, mapping software and use of the high spatial resolution maps produced for particular types of geospatial modeling.

The Customer Journey Mapping Software market is an increasingly vital segment within the broader field of customer experience management. As organizations strive to enhance their interactions with consumers, these software solutions provide valuable insights into the entire customer journey-from initial awareness th

This data layer is part of a collection of GIS data created for the Okanagan Mainstem Floodplain Mapping Project. Notes below apply to the entire project data set.***Download Size is 12.5 GBGeneral Notes1. Please refer to the Disclaimer further below.2. Please review the associated project reports before using the floodplain maps: Northwest Hydraulic Consultants Ltd. (NHC). 2020. ‘Okanagan Mainstem Floodplain Mapping Project’. Report prepared for the Okanagan Basin Water Board (OBWB). 31 March 2020. NHC project number 3004430. Northwest Hydraulic Consultants Ltd. (NHC). 2021. ‘Okanagan Mainstem Floodplain Mapping Project – Development of CGVD1928 Floodplain Mapping’. Letter report prepared for the Okanagan Basin Water Board (OBWB). 30 March 2021. NHC project number 3006034.Northwest Hydraulic Consultants Ltd. (NHC). 2022. ‘Supplemental to the Okanagan Mainstem Floodplain Mapping Project – Current Operations Flood Construction Levels for Okanagan and Wood-Kalamalka Lakes’. Report prepared for the Okanagan Basin Water Board (OBWB). Final. 16 August 2022. NHC project number 3006613.3. These floodplain mapping layers delineate flood inundation extents under the specific flood events. Tributaries are not included in mapping.4. The mapped inundation is based on the calculated water level. Freeboard, wind effects, and wave effects have been added to the calculated water level where noted.5. Where noted, a freeboard allowance of 0.6 m has been added to the calculated flood water level. It has been added to account for local variations in water level and uncertainty in the underlying data and modelling.6. Where noted, the FCL (or COFCL) included in lake mapping layers includes an allowance for wind setup and wave runup based on co-occurrence of the seasonal 200-year wind event. The wind and wave effects extend 40 m shoreward to delineate the expected limit of wave effects. Beyond this limit the FCL (or COFCL) is based on inundation of the flood event without wave effects. Wave effects have been calculated based on generalized shoreline profile and roughness for each shoreline reach. Site specific runup analysis by a Qualified Registrant may be warranted to refine the generalized wave effects shown, which could increase or decrease the FCL (or COFCL) by as much as a metre.7. Underlying hydraulic analysis assumes channel and shoreline geometry is stationary. Erosion, deposition, degradation, and aggradation are expected to occur and may alter actual observed flood levels and extents. Obstructions, such as log-jams, local storm water inflows or other land drainage, groundwater, or tributary flows may cause flood levels to exceed those indicated on the maps.8. The Okanagan floodplain is subject to persistent ponding due to poor drainage. Persistent ponding is not covered by the flood inundation mapping.9. For flood level maps (water level and inundation extents):a. Layers for each flood scenario describe inundation extents, water surface elevations, and depths.b. The calculated water level has been extended perpendicular to flow across the floodplain; thus mapping inundation of isolated areas regardless of likelihood of inundation; whether it be from dike failure, seepage, or local inflows. Distant isolated areas may be conservatively mapped as inundated. Site specific judgement by a Qualified Professional is required to determine validity of isolated inundation.c. Filtering was used to remove isolated areas smaller than 100 m2. Holes in the inundation extent with areas less than 100 m2 were also removed. Isolated areas larger than 100 m2 are included in GIS data layers and are shown on maps if they are within 40 metres of direct inundation or within 40 metres of other retained polygons.d. Okanagan Dam breach, dam overtopping, or overtopping and breaching of Penticton Beach were not modelled. Inundation downstream of the Okanagan Dam on the left bank floodplain is based on river modelling with the assumption that Okanagan Lake levels will not overtop Lakeshore Drive and adjacent high ground. For the design flood scenarios, inundation mapping on the right bank of the Okanagan River from the Okanagan Dam downstream to the Highway 97 bridge and Burnaby Avenue is based on additional lake and river modelling. For other flood scenarios, river and lake inundation has been mapped separately and has not been integrated on the right bank. Inundation mapping on the right bank is based on river modelling as far as the most upstream modelled river cross section.10. For flood hazard maps (depth and velocity):a. Layers describe flood water depths and velocities. Depths and velocities are based on the maximum values from three modelled scenarios: all dikes removed, left bank dikes removed, and right bank dikes removed. Depths do not include freeboard.b. All hazard layers were modelled with the same parameters and boundary conditions as the design flood.11. Flood modelling and mapping is based on a digital elevation model (DEM) with the following coordinate system and datum specifications: Universal Transverse Mercator Zone 11-N (UTM Zone 11-N), North American Datum 1983 Canadian Spatial Reference System epoch 2002.0 (NAD83 CSRS (2002.0)), Canadian Geodetic Vertical Datum 2013 (CGVD2013), Canadian Gravimetric Geoid model of 2013 (CGG2013). FCL values are presented on the maps in both CGVD2013 and CGVD1928 vertical datums. CGVD1928 values are based on the following specifications: NAD83 CSRS (2002.0), CGVD1928, Height Transformation version 2.0 epoch 1997 (HTv2.0 (1997)). COFCL and COFCL values are presented only in CGVD2013.12. The accuracy of simulated flood levels is limited by the reliability and extent of water level, flow, and climatic data. The accuracy of the floodplain extents is limited by the accuracy of the design flood flow, the hydraulic model, and the digital surface representation of local topography. Localized areas above or below the mapped inundation maybe generalized. Therefore, floodplain maps should be considered an administrative tool that indicates flood elevations and floodplain boundaries for a designated flood. A qualified professional is to be consulted for site-specific engineering analysis.13. Industry best practices were followed to generate the floodplain maps. However, actual flood levels and extents may vary from those shown. OBWB and NHC do not assume any liability for variations of flood levels and extents from that shown.Data Sources Design flood events are based on hydrologic modelling of the Okanagan River watershed. The hydraulic response is based on a combination of 1D and 2D numerical models developed by NHC using HEC-RAS software, and NHC SWAN models. The hydraulic models are calibrated to the 2017 flood event and validated to the 2018 flood event; due to limits on data availability the hydrologic model is calibrated using data from 1980-2010. The digital elevation model (DEM) used to develop the model and mapping is based on Lidar data collected from March to November 2018 and provided by Emergency Management BC (EMBC), channel survey conducted by WSP in March, April, and June 2019, and additional survey data. See accompanying report for details NHC (2020).DisclaimerThis document has been prepared by Northwest Hydraulic Consultants Ltd. for the benefit of Okanagan Basin Water Board, Regional District of North Okanagan, Regional District of Central Okanagan, Regional District of Okanagan-Similkameen, Okanagan Nation Alliance for specific application to the Okanagan Mainstem Floodplain Mapping Project, Okanagan Valley, British Columbia, Canada (Ellison, Wood, Kalamalka, Okanagan, Skaha, Vaseux, and Osoyoos lakes and Okanagan River from Okanagan Lake to Osoyoos Lake). The information and data contained herein represent Northwest Hydraulic Consultants Ltd. best professional judgment in light of the knowledge and information available to Northwest Hydraulic Consultants Ltd. at the time of preparation, and was prepared in accordance with generally accepted engineering practices.Except as required by law, this document and the information and data contained herein are to be treated as confidential and may be used and relied upon only by Okanagan Basin Water Board, Regional District of North Okanagan, Regional District of Central Okanagan, Regional District of Okanagan-Similkameen, Okanagan Nation Alliance, its officers and employees. Northwest Hydraulic Consultants Ltd. denies any liability whatsoever to other parties who may obtain access to this document for any injury, loss or damage suffered by such parties arising from their use of, or reliance upon, this report or any of its contents.Data Layer List and Descriptions<!--· River / Lake Model Boundary -River / Lake Model Boundary (NHC): Boundary between Okanagan River and Okanagan Lake modelling and mapping areas for design and flood mapping.Recommended Design Flood (gates open): Ellison, Skaha, Vaseux, and Osoyoos lakeso Lake Shoreline Flood Construction Level (FCL) Zone – Recommended Design Flood with Freeboard and Wave Effect (NHC): Zone defined based on approximate shoreline and the wave breaking boundary plus a buffer; FCLs defined by zone along shoreline; shoreline FCLs take precedence over lake inundation FCLs.o Lake Flood Construction Level (FCL) Zone (Inundation Extent) – Recommended Design Flood with Freeboard (NHC): Design flood inundation extent with freeboard. Design event varies by lake, plus wind setup, plus mid-century climate change; plus freeboard 0.6m.o Lake Inundation Extent – Recommended Design Flood without Freeboard (NHC): Design flood inundation extent without freeboard. Design event varies by lake, plus wind setup, plus mid-century climate change.o Depth Grids§ Ellison Lake Depth – Recommended Design without Freeboard (NHC): ELLISON LAKE: 200-YEAR MID-CENTURY. Design flood depth

Healthcare Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain point geographic files of healthcare organizations, providers, and hospitals and an boundary file of Primary Care Service Areas.

High-density linkage maps are important tools for genome biology and evolutionary genetics by quantifying the extent of recombination, linkage disequilibrium and chromosomal rearrangements across chromosomes, sexes and populations. They provide one of the best ways to validate and refine de novo genome assemblies, with the power to identify errors in assemblies increasing with marker density. However, assembly of high-density linkage maps is still challenging due to software limitations. We describe Lep-MAP2, a software for ultra-dense genome-wide linkage map construction. Lep-MAP2 can handle various family structures and can account for achiasmatic meiosis to gain linkage map accuracy. Simulations show that Lep-MAP2 outperforms other available mapping software both in computational efficiency and accuracy. When applied to two large F2-generation recombinant crosses between two nine-spined stickleback (Pungitius pungitius) populations, it produced two high-density (~6 markers/cM) linkag...

The global market for EV route planner apps is experiencing robust growth, fueled by the accelerating adoption of electric vehicles (EVs). While precise market sizing data is unavailable, a reasonable estimation based on the current EV market expansion and the increasing need for sophisticated navigation tools suggests a 2025 market value of approximately $250 million. Considering a projected Compound Annual Growth Rate (CAGR) of 25% (a conservative estimate given industry trends), this market is poised to reach approximately $1.2 billion by 2033. Key drivers include the expanding EV infrastructure (charging stations), growing consumer demand for convenient and reliable EV charging solutions, and increasing government incentives promoting EV adoption. Emerging trends include the integration of real-time charging station availability, advanced route optimization algorithms considering factors like battery range, charging speeds and energy consumption, and the incorporation of features like payment integration and seamless user experience. Restraints currently include the uneven distribution of charging infrastructure in some regions, the varying standards and compatibility issues across different charging networks, and potential concerns regarding data privacy related to location and charging history. The competitive landscape is highly dynamic, featuring a mix of established players like ChargePoint, Tesla, and Shell Recharge alongside newer entrants and niche players. Companies are differentiating themselves through innovative features, partnerships with charging networks, and robust user interfaces. The market is further segmented by app functionality (basic route planning vs. advanced features), pricing model (free vs. subscription-based), and geographic regions, with North America and Europe currently leading in adoption but significant growth anticipated in Asia-Pacific and other developing markets. The success of individual apps hinges on factors like accuracy of charging station data, ease of use, comprehensive feature sets, and effective marketing strategies to reach the growing target audience of EV owners and potential buyers. Future growth will likely be driven by improvements in app functionality, expansion into new markets, and strategic collaborations within the broader EV ecosystem.

Chapter 3: Multiuser Concepts and Workflow Replicability in sUAS Applications Jason A. Tullis, Katie Corcoran, Richard Ham, Bandana Kar, and Malcolm Williamson Advances in high spatial resolution mapping capabilities and the new rules established by the Federal Aviation Administration in the United States for the operation of Small Unmanned Aircraft Systems (sUAS) have provided new opportunities to acquire aerial data at a lower cost and more safely versus other methods. A similar opening of the skies for sUAS applications is being allowed in countries across the world. Also, sUAS can access hazardous or inaccessible areas during disaster events and provide rapid response when needed. Applications of Small Unmanned Aircraft systems: Best Practices and Case Studies is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. Very few sUAS pilots have the knowledge of how the collected imagery is processed into value added mapping products that have commercial and/or academic import. Since the field of sUAS applications is just a few years old, this book covers the need for a compendium of case studies to guide the planning, data collection, and most importantly data processing and map error issues, with the range of sensors available to the user community. Written by experienced academics and professionals, this book serves as a guide on how to formulate sUAS based projects, from choice of a sUAS, flight planning for a particular application, sensors and data acquisition, data processing software, mapping software and use of the high spatial resolution maps produced for particular types of geospatial modeling.

This application is used to help people locate heat relief from May to September in Maricopa County. The Heat Relief Network is made up of municipalities, nonprofit organizations, faith-based community, and businesses and is kept up to date throughout the summer. This is a custom JavaScript application built in-house by Jack Fairfield. Survey123 is used to gather the Heat Relief Network partner applications. The feature service is maintained and updated through portal on a hosted ArcGIS Server.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).