Duplicate the Projection.prj file and rename the duplicate to the same name as the ASCII grid, e.g. MAT.asc and MAT.prj. When MAT.asc is imported to ESRI ArcGIS or QGIS, the GIS systems will automatically pick-up the correct grid projection.

The outlines of the craters as a shapefile (PRJ file).

This dataset is comprised of the .prj and .cvf files used to build the database for the Virus Particle Exposure in Residences (ViPER) Webtool, a single zone indoor air quality and ventilation analysis tool developed by the National Institute of Standards and Technology (NIST).

This submission contains an update to the previous Exploration Gap Assessment funded in 2012, which identify high potential hydrothermal areas where critical data are needed (gap analysis on exploration data).

The uploaded data are contained in two data files for each data category: A shape (SHP) file containing the grid, and a data file (CSV) containing the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to five data types:

1. well data
2. geologic maps
3. fault maps
4. geochemistry data
5. geophysical data Associated shapefile projection file.

Rifts mapped through reviewing the location of dikes and vents on the USGS 2007 Geologic Map of the State of Hawaii, as well as our assessment of topography, and, to a small extent, gravity data. Data is in shapefile format. Hawaii rifts shapefile projection file

This dataset provides shapefile of outlines of the 68 lakes where temperature was modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

This dataset provides shapefile outlines of the 7,150 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).

This directory contains the following maps in GeoTIFF format: open-water frequency, time-series of reflectance for each band (Red and NIR), and frequency of SSSC classes.

Fassoni-Andrade, A. C., & Paiva, R. C. D. (2019). Mapping spatial-temporal sediment dynamics of river-floodplains in the Amazon. Remote Sensing of Environment, 221, 94-107.

https://www.sciencedirect.com/science/article/abs/pii/S0034425718305005

Corresponding autor: alice.fassoni@ufrgs.br

1) Data Description:

1.1) Spatial Representation Type: Raster Format: TIFF Columns and rows: 7661, 2405 Cell Size (X,Y): 0.002245, 0.002245 (~250m)

1.2) Extent (coordinate system): Top: -0.025 Left: -67.28 Right: -50.081055 Botton: -5.424225

1.3) Spatial Reference Properties (GCS_WGS_1984.prj file): Type: Geographic Geographic Coordinate Reference: WGS 1984 Open Geospatial Consortium (OGC) Well Known Text (WKT): GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137.0,298.257223563]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433], AUTHORITY["EPSG",4326]]

2) Data description for individual files:

2.1) File: open_water_frequency.tif This map indicates for how long, during 15 years (2003-2017), each pixel in rivers and lakes of central Amazon basin remained as open-water at every four days. Number of Bands: 1 Values between 0 and 100. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

2.2) File: class_SSC_frequency.tif This map represents a 15-year frequency (2003-2017) at which each pixel in rivers and lakes of central Amazon basin remains in one of the surface suspended sediments concentration classes (SSSC): high, moderate, and low. The open-water frequency map must be considered to interpret the sediments temporal dynamics in the class frequency map. For example, a pixel in the floodplain lake with frequency of 10, 30, and 20% in SSSC classes low, medium and high respectively, remains 40% of the time as no open-water. Number of Bands: 3 band 1: low SSSC class; band 2: Moderate SSSC class; band 3: High SSSC class Composition of bands for best visualization: R(3)G(2)B(1) without contrast Values between 0 and 100. Pixel Type: double precision Pixel Depth: 64 bit No Data Value: 0

2.3) File: time_series_nir.tif This map represents the climatology time series of infrared (nir) reflectance in period of four-days in rivers and lakes of central Amazon basin between 2003 and 2017 (15 years). Number of Bands: 92 (each band represent a date that is identified in dates.txt file) Values between 0 and 10000. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

2.4) File: time_series_red.tif This map represents the climatology time series of red reflectance in period of four-days in rivers and lakes of central Amazon basin between 2003 and 2017 (15 years). Number of Bands: 92 (each band represent a date that is identified in dates.txt file) Values between 0 and 10000. Pixel Type: floating point Pixel Depth: 32 bit No Data Value: 0

Context

This dataset contains shapefiles outlining 558 neighborhoods in 50 major cities in New York state, notably including Albany, Buffalo, Ithaca, New York City, Rochester, and Syracuse. This adds context to your datasets by identifying the neighborhood of any locations you have, as coordinates on their own don't carry a lot of information.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. What fields does it include? What's the time period of the data and how was it collected?

Four files are included containing data about the shapes: an SHX file, a DBF file, an SHP file, and a PRJ file. Including all of them in your input data are necessary, as they all contain pieces of the data; one file alone will not have everything that you need.

Seeing how none of these files are plaintext, it can be a little difficult to get set up with them. I highly recommend using mapshaper.org to get started- this site will show you the boundaries drawn on a plane, as well as allow you to export the files in a number of different formats (e.g. GeoJSON, CSV) if you are unable to use them in the format they are provided in. Personally, I have found it easier to work with the shapefile format though.

To get started with the shapefile in R, you can use the the rgdal and rgeos packages. To see an example of these being used, be sure to check out my kernel, "Incorporating neighborhoods into your model".

Acknowledgements

These files were provided by Zillow and are available under a Creative Commons license.

Inspiration

I'll be using these in the NYC Taxi Trip Duration competition to add context to the pickup and dropoff locations of the taxi rides and hopefully greatly improve my predictions.

The Columbia River Light Detection and Ranging (LiDAR) survey project was a collaborative effort to develop detailed high density LiDAR terrain data for the US Army Corps of Engineers (USACE). The LiDAR will be used to support hydraulic modeling work associated with proposed 2014 Columbia River treaty negotiations. The dataset encompasses approximately 2836 square miles of territory in portions of Oregon, Washington, Idaho and Montana within the Columbia River drainage. This survey was under the jurisdiction of three Corps districts: Portland (CENWP), Seattle (CENWS), and Walla Walla (CENWW). CENWP was the project lead and primary contracting organization. Bare earth point data are classified as either ground (2), model key point (8) or water (9) and represent the earth's surface with all vegetation and human-made structures removed. Model key points were generated to represent the bare earth surface within a 0.07 m tolerance. Ground points (class 2) are the remaining ground points not classed as model key. Both ground and model key classes are needed for display of all bare earth points. Water classification was used for those bare earth/ground classified points that fell inside a water boundary as determined using softcopy photogrammetry with stereograms generated from LiDAR intensities. All remaining points received the default classification (1). In some areas of heavy vegetation or forest cover, there may be relatively few ground points in the LiDAR data. The RMSE of the data for open, hard-packed surfaces is 0.046 meters as assessed from 40,266 ground survey (real time kinematic) points taken on hard-packed road surfaces. This value is representative of anticipated accuracies in open, evenly sloped or flat terrain where maximum point densities were achieved. The project was completed for the US Army Corps of Engineers, Portland District, to support hydraulic modeling related to the ACOE Columbia River Treaty project. Data acquisition, bare earth processing, and development of final tiled LiDAR deliverables and DEM's was performed by Watershed Sciences, Inc. Overall project management, photogrammetric quality control review using LiDAR stereograms, water delineation and breakline development was performed by David C. Smith & Associates, Inc. Professional Surveyor oversight of ground control data, ground control data processing and ground control publication was performed by David Evans and Associates, Inc. Final quality control review in ArcGIS of all final deliverables, including preparation of point density rasters and reach based geo-databases incorporating all deliverables, was performed by CC Patterson and Associates. NOTE ON DATUM ISSUES: All ground control and subsequent LiDAR data deliverables were developed and delivered at NAD '83 CORS 96 horizontal and NAVD '88 Geoid '09 vertical datums as processed in OPUS-DB. Due to limitations in the transformations supported by ESRI, NAD '83 and NAVD '88 datums were temporarily assigned to the ESRI deliverables and ESRI .prj file even though the actual coordinate values in the data files are at the original NAD '83 CORS 96 and NAVD '88 Geoid '09 datums. In many instances, a temporary assignment of NAD '83 HARN or HPGN may better approximate local conditions. Plain NAD '83 was used for the primary deliverable in order to avoid any implication of higher precision; however, the user may want to evaluate other approximations for specific applications. At such time as ESRI includes support for NAD '83 CORS '96, the temporary NAD '83 assignment in the .prj file should be replaced with NAD '83 CORS '96 without further reprojection. The NOAA Coastal Services Center has converted the data to ellipsoid heights (using Geoid09) and NAD 83 geographic coordinates for data storage and Digital Coast provisioning purposes.

This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of the Americas showing the location of ultramafic soils in Canada, the United States of America, Mexico, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The R code used to compile the dataset as well as an image of the compiled dataset are also included. , The data are derived from ten geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined., , README: Geodatabase of ultramafic soils of the Americas

Author: Catherine Hulshof, Virginia Commonwealth University, cmhulshof@vcu.edu

Abstract: This is a compiled geospatial dataset in ESRI polygon shapefile format of ultramafic soils of many countries in the Americas showing the location of ultramafic soils in Canada, the United States of America, Guatemala, Cuba, Dominican Republic, Puerto Rico, Costa Rica, Colombia, Argentina, Chile, Venezuela, Ecuador, Brazil, Suriname, French Guiana, and Bolivia. The data are derived from nine geospatial datasets. Original datasets were subset to include only ultramafic areas, datasets were assigned a common projection (WGS84), attribute tables were reconciled to a common set of fields, and the datasets were combined.

Contents: The data are in ESRI shapefile format and thus have four components with extensions .shp, .shx, .prj, and .dbf. The .shp file contains the feature geometries, the .prj file contains the geographic coordin...

Large-scale_prediction_archiveThis compressed archive includes multiple other files including data files (in .rdata format) GIS shapefiles (in folders with the associated .shp, .shx, .dbf, and .prj files for each map) and an R script that will run all analyses and plot all figures. Specific descriptions of each file are supplied in the README.TXT file.

This dataset mainly includes the twice a day (ascending-descending orbit) brightness temperature (K) of the space-borne microwave radiometers SSM / I and SSMIS carried by the US Defense Meteorological Satellite Program satellites (DMSP-F08, DMSP-F11, DMSP-F13, and DMSP-F17), time coverage from September 15, 1987 to December 31, 2015. The SSM/I brightness temperature of DMSP-F08, DMSP-F11 and DMSP-F13 include 7 channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 85.50H and 85.50V; The SSMIS brightness temperature observation of DMSP-F17 consists of seven channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 91.66H and 91.66v. Among them, DMSP-F08 satellite brightness temperature coverage time is from September 15, 1987 to December 31, 1991; DMSP-F11 satellite brightness temperature coverage time is from January 1, 1992 to December 31, 1995; The coverage time of DMSP-F13 satellite brightness temperature is from January 1, 1996 to April 29, 2009; The coverage time of DMSP-F17 satellite brightness temperature is from January 1, 2009 to December 31, 2015. 1. File format and naming: The brightness temperature is stored separately in units of years, and each directory is composed of remote sensing data files of each frequency, and the SSMIS data also contains the .TIM time information file. The data file names and their naming rules are as follows: EASE-Fnn-ML / HyyyydddA / D.subset.ccH / V (remote sensing data) EASE-Fnn-ML / HyyyydddA / D.subset.TIM (time information file) Among them: EASE stands for EASE-Grid projection method; Fnn stands for satellite number (F08, F11, F13, F17); ML / H stands for multi-channel low-resolution and multi-channel high-resolution respectively; yyyy represents the year; ddd represents Julian Day of the year (1-365 / 366); A / D stands for ascending (A) and descending (D) respectively; subset represents brightness temperature data in China; cc represents frequency (19.35GHz, 22.24 GHz, 37.05GHz, (85.50GHz, 91.66GHz); H / V stands for horizontal polarization (H) and vertical polarization (V), respectively. 2. Coordinate system and projection: The projection method of this data set is EASE-Grid, which is an equal area secant cylindrical projection, and the double standard parallels are 30 ° north and south. For more information about EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection to Geographic projection, please refer to the ease2geo.prj file, the content is as follows: Input projection cylindrical units meters parameters 6371228 6371228 1 / * Enter projection type (1, 2, or 3) 0 00 00 / * Longitude of central meridian 30 00 00 / * Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd parameters end 3. Data format: Stored as integer binary, Row number: 308 *166，each data occupies 2 bytes. The actual data stored in this dataset is the brightness temperature * 10. After reading the data, you need to divide by 10 to get the real brightness temperature. 4. Data resolution: Spatial resolution: 25.067525km, 12.5km (SSM / I 85GHz, SSMIS 91GHz) Time resolution: daily, from 1978 to 2015. 5. Spatial range: Longitude: 60.1 ° -140.0 ° east longitude; Latitude: 14.9 ° -55.0 ° north latitude. 6. Data reading: Remote sensing image data files in each set of data can be opened in ArcMap, ENVI and ERDAS software.

Faults combined from USGS 2007 Geologic Map of the State of Hawaii and the USGS Quaternary Fault and Fold database. This data is in shapefile format. Hawaii faults shapefile projection file

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

These data are a collection of benthic habitat data from studies conducted in the coastal Long Island Sound, NY region in GIS shapefile (.shp, .dbf, .shx, and .prj files) with associated Federal Geographic Data Committee (FGDC) metadata. Generalized browse graphics were generated by the NODC and are included with the data. Individual subdirectories include data as follows - 2002 Long Island South Shore Estuary Benthic Habitat Polygon Data Set, 1995 benthic grab, sediment grab, and sediment profile image GIS point data files from inland harbor bays (Jamaica Bay), and 1994-1995 benthic grab, sediment grab, and sediment profile image GIS point data files from lower inland harbor bays.

Data content: the depth to the permafrost table and the spatial distribution of taliks in the headwaters of the Yellow River in the current year and under the scenario of 1, 2 and 3 ℃ temperature rise. This data is calculated by using WaSiM model and considering the water and heat conditions in the headwatersof the Yellow River. The balance calculation time is 2010-2012, and the distribution of permafrost is named as 2011. Based on this, the scenarios of 1 ℃, 2 ℃ and 3 ℃ temperature increase are set respectively to calculate the balance of permafrost distribution. The data format is ESRI ASCII, and the projection coordinates are uniformly dpt_ Pesent2011.prj file. Data can be displayed directly in ArcMap.

description: These data are a collection of benthic habitat data from studies conducted in the coastal Long Island Sound, NY region in GIS shapefile (.shp, .dbf, .shx, and .prj files) with associated Federal Geographic Data Committee (FGDC) metadata. Generalized browse graphics were generated by the NODC and are included with the data. Individual subdirectories include data as follows - 2002 Long Island South Shore Estuary Benthic Habitat Polygon Data Set, 1995 benthic grab, sediment grab, and sediment profile image GIS point data files from inland harbor bays (Jamaica Bay), and 1994-1995 benthic grab, sediment grab, and sediment profile image GIS point data files from lower inland harbor bays.; abstract: These data are a collection of benthic habitat data from studies conducted in the coastal Long Island Sound, NY region in GIS shapefile (.shp, .dbf, .shx, and .prj files) with associated Federal Geographic Data Committee (FGDC) metadata. Generalized browse graphics were generated by the NODC and are included with the data. Individual subdirectories include data as follows - 2002 Long Island South Shore Estuary Benthic Habitat Polygon Data Set, 1995 benthic grab, sediment grab, and sediment profile image GIS point data files from inland harbor bays (Jamaica Bay), and 1994-1995 benthic grab, sediment grab, and sediment profile image GIS point data files from lower inland harbor bays.