GIS Grid Files For The Sampling (NCEI Accession 0208321 and NCEI Accession 0208322)

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DRTO_Grid.dbf 2019-12-20 07:14 2.2M
DRTO_Grid.prj 2019-12-20 07:14 424
DRTO_Grid.sbn 2019-12-20 07:14 291K
DRTO_Grid.sbx 2019-12-20 07:14 17K
DRTO_Grid.shp 2019-12-20 07:14 4.0M
DRTO_Grid.shp.xml 2019-12-20 07:14 21K
DRTO_Grid.shx 2019-12-20 07:14 243K
FlaKeys_Grid.dbf 2019-12-20...

2016, 2015. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project city-level data in GIS-friendly format can be joined with city spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-City-Boundaries/n44h-hy2j) in a geographic information system (GIS) to produce maps of 27 measures at the city-level. There are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) in this 2018 release from the 2015 BRFSS that were the same as the 2017 release.

This statistic illustrates the share of municipalities using Geographic Information Systems (GIS) tools in Italy in selected years between 2009 and 2018. In 2018, 34.4 percent of the municipalities located in Italy reported using GIS tools in their public administration.

Smart agriculture refers to tools that collect, store and analyze digital data along the agricultural value chain. Geographic Information System (GIS) system software is one of those tools used in the agricultural sector. The GIS System market in Spain had a value of over 36 million dollars in 2019.

Cette base de données représente la situation des données (shapefile et divers documents) de l'AFI de la RDC au 31 Décembre 2018.

The GIS software market in Agriculture in Latin America was estimated at around 130 million U.S. dollars in 2018, and was forecast to surpass 143 million dollars in 2019. In the latter year, Brazil was expected to account for nearly one third of this market, with a value of 47.8 million dollars. Meanwhile, Argentina's market was forecast at 33.3 million dollars in 2019.

Analysis of ‘500 Cities: Census Tract-level Data (GIS Friendly Format), 2018 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/026fa141-2bee-4387-bf52-3095ae0f6b32 on 12 February 2022.

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

2016, 2015. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, cholesterol screening) in this 2018 release from the 2015 BRFSS that were the same as the 2017 release.

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

The GIS database has been developed under the project "Renewable Energy Mapping: Small Hydro Tanzania". This study is part of a technical assistance project, ESMAP funded, being implemented by Africa Energy Practice of the World Bank in Tanzania which aims at supporting resource mapping and geospatial planning for small hydro. Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_TNZ The GIS database contains the following datasets: Administrative Boundaries Hydrology Protected Areas Satellite Imagery Land Cover Geology Topography Population Infrastructure: Power/ Transport each accompanied by a metadata file Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Tanzania Small Hydro GIS Atlas, 2018, https://energydata.info/dataset/tanzania-small-hydro-gis-database-2018"

In this edition we highlight Ashley Kershner and the Downtown Lynchburg Association and their use of GIS in creating a storefront occupancy inventory. We also touch on Parks and Rec exploring where their activity patrons are from via GIS, as well as some cool tech the GIS office has for you to borrow to collect GPS data.

The Digital Quaternary Surficial Geologic-GIS Map of Santa Barbara Island, California 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 (saba_surficial_geology.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 (saba_surficial_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (saba_surficial_geology.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.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.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 (saba_surficial_geology_metadata_faq.pdf). Please read the chis_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 (saba_surficial_geology_metadata.txt or saba_surficial_geology_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 1:12,000 and United States National Map Accuracy Standards features are within (horizontally) 6.1 meters or 20 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).

Aerial imagery captured February 2018 to March 2018 when the leaves are off the trees (leaf-off flyover). Any questions please call the Onslow County GIS Department at 1-910-937-1190, Monday - Friday 8am - 5pm.

In this edition we highlight the ways Community Development uses GIS in the Downtown Master Plan as well as the use of GIS during natural disasters.

Basic information from 2018 CAMA (Computer Aided Mass Appraisal) database including PIN (Map_Route), Site Address, Owner, Mailing Address, recent sale, tax district, and exemption status; this can be joined to GIS Parcel layer via Map_Route.For more information about the Macon-Bibb County Board Of Tax Assessors visit their website at http://www.co.bibb.ga.us/TaxAssessors or call (478) 621-6701.

This dataset tracks the updates made on the dataset "500 Cities: Census Tract-level Data (GIS Friendly Format), 2018 release" as a repository for previous versions of the data and metadata.

In this edition, we highlight the collaboration between GIS and various departments to develop a strategic plan for the GIS division. We also touch on newly acquired historical imagery, how Water Resources is using GIS to inform citizens, as well as updates to the GIS Academy.

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

This dataset contains model-based census tract level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

This layer is a high-resolution tree canopy change-detection layer for Cambridge, Massachusetts. It contains three tree-canopy classes for the period 2014-2018: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2014 and 2018 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2014 and 2018 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.

This global layer depicts the global network of submarine communication cables and terminals, as of June 2018. The data was prepared by TeleGeography and published here. This layer is a cartographic representation of the actual data. To purchase the authoritative data, please visit TeleGeography's Global Bandwidth Research Service product page.