This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

ArcGIS Dashboards Training Videos for COVID-19With the current COVID-19 situation across the world, there’s been a proliferation of corona virus themed dashboards emerging over the last few weeks in ArcGIS Online. Many of these were created with ArcGIS Dashboards, which enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen._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...

An ArcGIS Blog tutorial that guides you through creating your first dashboard using ArcGIS Dashboards.ArcGIS Dashboards is a configurable web app available in ArcGIS Online that enables users to convey information by presenting interactive charts, gauges, maps, and other visual elements that work together on a single screen.In this tutorial you will create a simple dashboard using ArcGIS Dashboards. The dashboard uses a map of medical facilities in Los Angeles County (sample data only) and includes interactive chart and list elements._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...

ArcGIS Technology for Mapping COVID-19 (Esri Training).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. This plan will teach you the core ArcGIS technology necessary to understand, prepare for, and respond to COVID-19 in your community or organization.More information about Esri training..._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...

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

[Metadata] Locations of golf courses in the State of Hawaii as of August 2023. Source: Downloaded by Hawaii Statewide GIS Program staff from Hawaii State Golf Association website (https://hawaiistategolf.org), 8/8/23. NOTE: This data layer shows the status of golf courses BEFORE THE MAUI WILDFIRES OF AUGUST 2023. Geocoded using Esri's World Geocoder. Modified some locations based on satellite imagery, various road layers, etc. For more information, please see metadata at https://files.hawaii.gov/dbedt/op/gis/data/golf_courses.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Our Certification & Restoration Program currently licenses water and wastewater treatment plant operators as well as water distribution plants throughout Florida. Obtaining one of these licenses is a prerequisite to obtaining employment as a plant operator, excluding owner-operators.See Metadata for contact information.

A webpage intended to highlight the RUBA program and how to connect with its resources. This includes introducing to the Local Government Specialists (LGSs) at DCRA and which LGS services which communities, and an overview of different RUBA programs, grants, publications and trainings. Includes embeds or links to the following:LGS Headshots and Bios: LGS Headshots and Bios - Overview (arcgis.com)DCRA Local Government Assistance App: DCRA Local Government Assistance / RUBA Program (arcgis.com)RUBA Utility Management Training Courses Storymap: RUBA Utility Management Training Courses (arcgis.com)RUBA Publications Storymap: RUBA Publications (arcgis.com)RUBA Grant Report Summary Storymap: RUBA Grant Report Summary (arcgis.com)Best Practices Storymap: Best Practices (arcgis.com)

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center

This land cover data set is derived from the original raster based Globcover regional (Africa) archive. It has been post-processed to generate a vector version at national extent with the LCCS regional legend (46 classes). This database can be analyzed in the GLCN software Advanced Database Gateway (ADG), which provides a user-friendly interface and advanced functionalities to breakdown the LCCS classes in their classifiers for further aggregations and analysis.

The data set is intended for free public access.

The shape file's attributes contain the following fields: -Area (sqm) -ID -Gridcode (Globcover cell value) -LCCCode (unique LCCS code)

You can download a zip archive containing: -the shape file (.shp) -the ArcGis layer file with global legend (.lyr) -the ArcView 3 legend file (.avl) -the LCCS legend tables (.xls)

Supplemental Information:

This land cover product is a vector version (ESRI shape) of the Globcover archive that was published in 2008 as result of an initiative launched in 2004 by the European Space Agency (ESA). Globcover is currently the most recent (2005) and resoluted (300 m) datasets on land cover globally. Given the need of this valuable information for environmental studies, natural resources management and policy formulation, through activities of the Global Land Cover Network (GLCN) programme, the Globcover has been reprocessed to generate databases at national extent that can be analyzed through the Advanced Database Gateway software (ADG) by GLCN. ADG is a cross-cutting interrogation software that allows the easy and fast recombination of land cover polygons according to the individual end-user requirements. Aggregated land cover classes can be generated not only by name, but also using the set of existing classifiers. ADG uses land cover data with a Land Cover Classification System (LCCS) legend. The ADG software is available for download on the GLCN web site at http://www.glcn.org/sof_7_en.jsp

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Antonio Martucci

Data lineage:

This land cover database is provided as ESRI shape file (vector format) and derives from reprocessing the raster based Globcover database (regional version). Globcover has undergone the following process: a) vectoralization at the national extent using ESRI ArcGis (arcinfo) 9.3; b) topological reconstruction (custom AML scripts launched inside ArcGis-arcinfo 9.3); c) simplification of areas according to a minimum mapping unit of 0.1 skim (10 ha) (custom AML scripts launched inside ArcGis-arcinfo 9.3); application of the FAO/UNEP Land Cover Classification System (LCCS) legend (46 classes); final processing to assure full compatibility with the GLCN software Advanced Database Gateway (ADG).

Online resources:

Download - Land cover of United Republic of Tanzania - Shape file format

GLOBCOVER on the ESA Web site

Global Land Cover Network - GLCN

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

description: Filtered (2% slope and less) direct normal solar resource data for the Southwest United States This data provides filtered solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. Exclusions: Contiguous Area 1km squared; US Census Urban Areas; MRLC Multi Resolution Land Characteristics Consortium http://www.mrlc.gov/ (Urban Areas, Water Features, Wetlands); Argone National Lab ACEC Lands (Areas of Critical Environmental Concern) Federally Protected lands (FS - IRA (Inventoried Roadless Areas, FS - National Monument, FS - National Scenic Area, FS - Wilderness, FS - Wilderness Study Area, BLM - Wilderness, BLM - National Recreation Area, BLM - Forest Reserve, BLM - Wilderness Study Area, BLM - National Monument, BLM - National Conservation Area, FWS - National Wildlife Refuge, FWS - Waterfowl Production Area, FWS - Wildlife Management Area, FWS - Wilderness, FWS - Wilderness Study Area, FWS - Fish Hatchery, NPS - National Battlefield, NPS - National Battlefield Park, NPS - National Capital Park, NPS - National Historic Park, NPS - National Historic Site, NPS - National Lakeshore, NPS - National Mall, NPS - National Memorial, NPS - National Military Park, NPS - National Monument, NPS - National Park, NPS - National Parkway, NPS - National Preserve, NPS - National Recreation Area, NPS - National Reserve, NPS - National River, NPS - National Seashore, NPS - Wilderness, NPS - Wilderness Study Area, NPS - National Wild and Scenic River). ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.; abstract: Filtered (2% slope and less) direct normal solar resource data for the Southwest United States This data provides filtered solar resource averaged over surface cells of 0.1 degrees in both latitude and longitude, or about 10 km in size. Exclusions: Contiguous Area 1km squared; US Census Urban Areas; MRLC Multi Resolution Land Characteristics Consortium http://www.mrlc.gov/ (Urban Areas, Water Features, Wetlands); Argone National Lab ACEC Lands (Areas of Critical Environmental Concern) Federally Protected lands (FS - IRA (Inventoried Roadless Areas, FS - National Monument, FS - National Scenic Area, FS - Wilderness, FS - Wilderness Study Area, BLM - Wilderness, BLM - National Recreation Area, BLM - Forest Reserve, BLM - Wilderness Study Area, BLM - National Monument, BLM - National Conservation Area, FWS - National Wildlife Refuge, FWS - Waterfowl Production Area, FWS - Wildlife Management Area, FWS - Wilderness, FWS - Wilderness Study Area, FWS - Fish Hatchery, NPS - National Battlefield, NPS - National Battlefield Park, NPS - National Capital Park, NPS - National Historic Park, NPS - National Historic Site, NPS - National Lakeshore, NPS - National Mall, NPS - National Memorial, NPS - National Military Park, NPS - National Monument, NPS - National Park, NPS - National Parkway, NPS - National Preserve, NPS - National Recreation Area, NPS - National Reserve, NPS - National River, NPS - National Seashore, NPS - Wilderness, NPS - Wilderness Study Area, NPS - National Wild and Scenic River). ### License Info DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

CrimeMapTutorial is a step-by-step tutorial for learning

crime mapping using ArcView GIS or MapInfo Professional GIS. It was

designed to give users a thorough introduction to most of the

knowledge and skills needed to produce daily maps and spatial data

queries that uniformed officers and detectives find valuable for crime

prevention and enforcement. The tutorials can be used either for

self-learning or in a laboratory setting. The geographic information

system (GIS) and police data were supplied by the Rochester, New York,

Police Department. For each mapping software package, there are three

PDF tutorial workbooks and one WinZip archive containing sample data

and maps. Workbook 1 was designed for GIS users who want to learn how

to use a crime-mapping GIS and how to generate maps and data queries.

Workbook 2 was created to assist data preparers in processing police

data for use in a GIS. This includes address-matching of police

incidents to place them on pin maps and aggregating crime counts by

areas (like car beats) to produce area or choropleth maps. Workbook 3

was designed for map makers who want to learn how to construct useful

crime maps, given police data that have already been address-matched

and preprocessed by data preparers. It is estimated that the three

tutorials take approximately six hours to complete in total, including

exercises.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2009 Fresno County, east, land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM), Water Use Efficiency Branch (WUE). Digitized land use boundaries and associated attributes were gathered by staff from DWR’s South Central Region (SCRO), using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Prior to the summer field survey by SCRO, WUE staff analyzed Landsat 5 imagery to identify fields likely to have winter crops. The combined land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s WUE Land Use Unit and SCRO, under the supervision of Steve Ewert. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of eastern Fresno County conducted by DWR, South Central Regional Office staff, under the leadership of Steve Ewert, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2009. SCRO staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. Eastern Fresno County was surveyed using the 2006 two-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos as a base for the preliminary line work. Line work for this survey was digitized using ArcMap software. When the 2009 one-meter resolution NAIP aerial photography became available, this was used to review the digital land use data. 2. The western boundary of the survey area is defined by the western boundaries of DWR’s Detailed Analysis Units 235 and 237. The northern boundary of the survey area is defined in part by the county boundary and also by the northern boundaries of the following U.S. Geological Survey’s (U.S.G.S) 7.5’ quadrangles: Friant (U.S.G.S. No. 36119H6), Academy (U.S.G.S. No. 36119H5), Piedra (U.S.G.S. No. 36119G4) and Pine Flat Dam (U.S.G.S. No. 36119G3). The eastern boundary of the survey area is defined in part by the county boundary and also by the eastern boundaries of the following quadrangles: Academy (U.S.G.S. No. 36119H5), Pine Flat Dam (U.S.G.S. No. 36119G3) and Orange Cove North (U.S.G.S. No. 36119F3). The southern boundary of the survey area is defined by the county boundary. 3. Digital aerial photographs and land use field boundaries were copied onto laptop computers for field data collection. The staff took these laptops into the field and virtually all areas were visited to positively identify the agricultural land uses. Land use codes were digitized directly into the laptop computers using ArcMap software using a standardized digitizing process. Some staff took printed aerial photos into the field instead of laptops and wrote land use codes directly onto these photo field sheets. Attributes for these areas were digitized later in the office. The field visits occurred between July 2009 and January 2010. Urban areas were primarily mapped by photo interpretation. Sources of irrigation water were not mapped in this survey. 4. Shapefiles of the field boundary lines and point attributes of the survey data were brought into ARCINFO. Both quadrangle and survey-wide polygon shapefiles were created, and underwent quality checks. 5. Winter grain fields were mapped using an analysis of Landsat 5 imagery. Two major assumptions in the analysis were that 1.) Winter grain was grown on some of the fields where corn, sudan or tomatoes were grown during the summer or where fields were fallow during the summer. 2.) For the fields listed above, we assumed that fields with high winter canopy cover were grain fields. To detect the winter grain fields of eastern Fresno County for the 2009 land use survey, corn fields were queried from the initial shapefile of the land use survey and classified using Landsat 5 imagery. The corn field polygons were buffered in 30 meters to reduce edge effects on the classification. The buffering eliminated some of the smaller fields leaving 798 fields to be classified. The Landsat 5 image acquired on 04/23/2009 was selected as the most appropriate for mapping grain for this survey. Approximately 10 percent of the corn fields were non-randomly selected to represent winter grain and fallow fields. Using a false color infrared display, bright red fields were selected to represent grain and light blue (non-red) fields were selected to represent fallow fields. Using the Hawth’s tools function, the selected fields were randomly divided into training (60%) and accuracy assessment (40%) categories. The polygons were then converted into raster format from vector format. Using ERDAS Imagine, the raster files were used to mask the Landsat 5 image and create two subset Landsat images representing training fields only and training plus accuracy assessment fields. eCognition Developer version 8.0 software was used with the Landsat image of training fields to segment each field into smaller signature areas. Polygons representing these signature areas were exported from eCognition Developer and the attributes of grain or fallow were added to these polygons. Spectral signatures based upon Landsat 5 bands 1,2,3,4,5, and 7 were created using ERDAS Imagine 2010. After associating the signatures with the image of training and accuracy assessment fields combined, a supervised classification was performed using the maximum likelihood parametric rule to classify each pixel. Zonal attributes of the fields were calculated using the recoded image. Based on the zonal attribute plurality, fields were classified as either winter grain or winter fallow. When there were no errors in the identification of “grain” and “fallow” fields in the fields reserved for accuracy assessment, a supervised classification was performed on the Landsat pixels representing all summer corn fields. Landsat images of each classified corn field were visually inspected in ArcMap to determine the reasonableness of the classification results. In addition to the Landsat 5 scene acquired on 04/23/2009, scenes acquired on 08/10/2008, 11/14/2008, 03/06/2009, 03/22/2009, 04/07/2009, 05/09/2009, 05/25/2009 and 06/26/2009 were used for the visual review of the results. Using the above methods, 660 fields were identified as winter grain. In a second process, polygons representing 315 fields that had initially been mapped as fallow, sudan or tomatoes during the 2009 summer field work were selected from the original land use survey shapefile. These were combined with the polygons representing the previously selected training fields. The polygons were converted from vector to raster format. The resulting raster file was used to mask the April 23, 2009 Landsat 5 image using ERDAS Imagine to produce a subset image. This new image was associated with the signatures previously developed to classify winter grain fields, and a supervised classification of each pixel was performed using the maximum likelihood parametric rule. After recoding, zonal attributes were calculated for each polygon. Based on the plurality calculated for each field, fields were identified as either grain or fallow for the winter season. Polygons identified as grain fields were individually inspected in ArcMap to assure the reasonableness of the classification results. Using the above methods, 67 fields were identified as winter grain. Polygons representing all winter grain crops identified by classifying the Landsat 5 images were merged together. The original land use shapefile was updated by adding grain as a first crop to the selected polygons and moving the summer crop into the set of cells that represent a second crop. All field boundary changes were incorporated into the original shapefile. The area, perimeter and acreages were updated at the end of the process. 6. After quality control/assurance procedures were completed on each file, the

ArcGIS is fundamentally an open platform. Esri president Jack Dangermond discusses how Esri ensures that ArcGIS is interoperable with other technology that users might need to integrate with ArcGIS. Esri’s approach is to help users achieve their interoperability goals. Esri supports open standards like OGC, WWW, and ISO standards, as well as industry data standards. The software has open APIs so developers can extend and build on top of the data and tools, and the ArcGIS platform is extendable and embeddable. Open source tools are also available in GitHub.

Locations where people who were laid off from specific companies can get training or re-training and employment servicesThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.