Wayback imagery is a digital archive of the World Imagery basemap, enabling users to access more than 100 different versions of World Imagery archived over the past 10 years. Each record in the archive represents a version of World Imagery as it existed on the date it was published.This app offers a dynamic Wayback browsing and discovery experience where previous versions of the World Imagery basemap are presented within the map, along a timeline, and as a list. Versions that resulted in local changes are dynamically presented to the user based on location and scale. Preview changes by hovering over and/or selecting individual layers. When ready, one or more Wayback layers can be added to an export queue and pushed to a new ArcGIS Online web map. Browse, preview, select, and create, it’s all there!For more information on Wayback check out these articles.You can also find every Wayback tile layer in the Wayback imagery group.

This World Imagery Metadata layer provides detailed information for each image source in World Imagery (Wayback 2019-08-07). With this metadata layer, a user can point and click anywhere on the map to get additional information about the imagery at that location. However, the information provided is only relevant to World Imagery (Wayback 2019-08-07).World Imagery basemap provides one meter or better satellite and aerial imagery for much of the world, and lower resolution satellite imagery worldwide. This Metadata layer, and the associated Wayback imagery layer, represent a version of World Imagery content as of 2019-08-07.The World Imagery basemap is regularly updated with more current imagery. When and where updates occur, the previous imagery is replaced and is no longer visible. For many use cases, the updated imagery is more desirable and typically preferred. Other times, however, the previous imagery may support use cases that the new imagery does not. In these cases, a user may need to access a previous version of the World Imagery basemap.Wayback imagery is a digital archive of the World Imagery basemap, enabling users to access different versions of the basemap archived over the years. Each record in the archive represents the World Imagery basemap as it existed at each publication date.Wayback currently supports all updated versions of World Imagery dating back to August 07, 2019. There is an AGOL item for every version and each of these items can be viewed in the Wayback Imagery group.

A map amendment is a request for a zone change from one zone to another in a specific area of the District. Contains map amendments going back to 2000. New map amendments to this feature class once they are approved by the Zoning Commission and the Order is issued in the DC Register.

This dataset contains two versions of the map of Pittsburgh City Council Districts, the current one (dating from 2022) and an earlier one (dating from 2012), each in multiple formats. For older city council district maps going back to 2022, see https://data.wprdc.org/dataset/pittsburgh-city-council-district-map

This dataset was created by using a new IIASA tool, called “Street Imagery validation” (https://svweb.cloud.geo-wiki.org/) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets.

There are 3 main data records uploaded:

1. sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.
2. sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84
3. sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.

Fields:

• "id" – unique observation identifier;
• "imgSource" – source of imagery used for visual inspection;
• "imgLoc" – image location;
• "svImgDate" – image date;
• "imageIdKey" – image unique identifier;
• "submitedAt" – date of submission of crop type observation;
• "cropType" - crop type observation;
• "irrType" – irrigation type;
• "x", "y" – centroids of submitted polygons in WGS84.

This archived dataset contains magnetic and gravity imaging data for the Appalachian Basin, compiled using Poisson Wavelet Multiscale Edge Detection, referred to as 'worm' for brevity, and stored in a PostGIS database, along with shapefiles and CSVs of relevant data. The archive also includes regional earthquake data going back to 1973 and relevant world stress map data. These data are used in estimating the seismic hazards (both natural and induced) for candidate direct use geothermal locations in the Appalachian Basin Play Fairway Analysis by Jordan et al. (2015).

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Summary: This dataset contains an inventory of City of Los Angeles Sidewalks and related features (Access Ramps, Curbs, Driveways, and Parkways).Background: This inventory was performed throughout 2017 using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines. The dataset has not been updated since its creation.Description: The following provides more detail about the feature classes in this dataset. All features were digitized (“traced”) as observed in the orthophotography (digital aerial photos) and assigned the Parcel Identification Number (PIN) of their corresponding property:Sidewalk (polygon) – represents paved pedestrian walkways. Typical widths are between 3‐6 feet in residential areas and larger and more variable in commercial and high‐density traffic areas.Alley-Sidewalk (polygon) – represents the prevailing walkway or path of travel at the entrance/exit of an alley. Digitized as Sidewalk features but categorized as Alley Sidewalk and assigned a generic PIN value, ALLEY SIDEWALK.Corner Polygon (polygon) - feature created where sidewalks from two streets meet but do not intersect (i.e. at corner lots). There’s no standard shape/type and configurations vary widely. These are part of the Sidewalk feature class.In commercial and high‐density residential areas where there is only continuous sidewalk (no parkway strip), the sidewalk also functions as a Driveway.Driveway (polygon) – represents area that provides vehicular access to a property. Features are not split by extended parcel lot lines except when two adjacent properties are served by the same driveway approach (e.g. a common driveway), in which case they are and assigned a corresponding PIN.Parkway (polygon) – represents the strip of land behind the curb and in front of the sidewalk. Generally, they are landscaped with ground cover but they may also be filled in with decorative stone, pavers, decomposed granite, or concrete. They are created by offsetting lines, the Back of Curb (BOC) line and the Face of Walk (FOW). The distance between the BOC and FOW is measured off the aerial image and rounded to the nearest 0.5 foot, typically 6 – 10 feet.Curb (polygon) – represents the concrete edging built along the street to form part of the gutter. Features are always 6” wide strips and are digitized using the front of curb and back of curb digitized lines. They are the leading improvement polygon and are created for all corner, parkway, driveway and, sidewalk (if no parkway strip is present) features.Curb Ramp, aka Access Ramp (point) – represents the geographic center (centroid) of Corner Polygon features in the Sidewalk feature class. They have either a “Yes” or “No” attribute that indicates the presence or absence of a wheelchair access ramp, respectively.Fields: All features include the following fields...FeatureID – a unique feature identifier that is populated using the feature class’ OBJECTID fieldAssetID – a unique feature identifier populated by Los Angeles City staff for internal usePIND – a unique Parcel Identification Number (PIN) for all parcels within the City of L.A. All Sidewalk related features will be split, non-overlapping, and have one associated Parcel Identification Number (PIN). CreateDate – indicates date feature was createdModifiedDate – indicates date feature was revised/editedCalc_Width (excluding Access Ramps) – a generalized width of the feature calculated using spatial and mathematical algorithms on the feature. In almost all cases where features have variable widths, the minimum width is used. Widths are rounded to the nearest whole number. In cases where there is no value for the width, the applied algorithms were unable to calculate a reliable value.Calc_Length (excluding Access Ramps) – a generalized length of the feature calculated using spatial and mathematical algorithms on the feature. Lengths are rounded to the nearest whole number. In cases where there is no value for the length, the applied algorithms were unable to calculate a reliable value.Methodology: This dataset was digitized using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines.The general work flow is as follows:Create line work based on digital orthophotography, working from the face‐of‐curb (FOC) inward to the property right-of-way (ROW)Build sidewalk, parkway, driveway, and curb polygons from the digitized line workPopulate all polygons with the adjacent property PIN and classify all featuresCreate Curb Ramp pointsWarnings: This dataset has been provided to allow easy access and a visual display of Sidewalk and related features (Parkways, Driveway, Curb Ramps and Curbs). Every reasonable effort has been made to assure the accuracy of the data provided; nevertheless, some information may not be accurate. The City of Los Angeles assumes no responsibility arising from use of this information. THE MAPS AND ASSOCIATED DATA ARE PROVIDED WITHOUT WARRANTY OF ANY KIND, either expressed or implied, including but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Other things to keep in mind about this dataset are listed below:Obscured Features – The existence of dense tree canopy or dark shadows in the aerial imagery tend to obscure or make it difficult to discern the extent of certain features, such as Driveways. In these cases, they may have been inferred from the path in the corresponding parcel. If a feature and approach was completely obscured, it was not digitized. In certain instances the coloring of the sidewalk and adjacent pavement rendered it impossible to identify the curb line or that a sidewalk existed. Therefore a sidewalk may or may not be shown where one actually may or may not exist.Context: The following links provide information on the policy context surrounding the creation of this dataset. It includes links to City of L.A. websites:Willits v. City of Los Angeles Class Action Lawsuit Settlementhttps://www.lamayor.org/willits-v-city-la-sidewalk-settlement-announcedSafe Sidewalks LA – program implemented to repair broken sidewalks in the City of L.A., partly in response to the above class action lawsuit settlementhttps://sidewalks.lacity.org/Data Source: Bureau of EngineeringNotes: Please be aware that this dataset is not actively being maintainedLast Updated: 5/20/20215/20/2021 - Added Calc_Width and Calc_Length fieldsRefresh Rate: One-time deliverable. Dataset not actively being maintained.

This data set provides weekly snow cover extent for the Northern Hemisphere in NetCDF format from September 1980 through most recent processing at a 24 km resolution.

The item published is a dataset that provides the raw data and original code to generate Figure 4 in the research paper, Correlative non-destructive techniques to investigate ageing and orientation effects in automotive Li-ion pouch cells, https://doi.org/10.5522/04/c.6868027 of which I am first author. The measurements and following data analysis took place between January 2022 – November 2022. The figure illustrates the ultrasonic mapping measurements of pouch cells that have been extracted from electric vehicles and have been aged in real-world conditions. The degradation of the cells was measured using four different complementary characterisation measurement techniques, one of which was ultrasonic mapping. The ultrasonic mapping measurements were performed using an Olympus Focus PX phased-array instrument (Olympus Corp., Japan) with a 5 MHz 1D linear phased array probe consisting of 64 transducers. The transducer had an active aperture of 64 mm with an element pitch (centre-to-centre distance between elements) of 1 mm. The cell was covered with ultrasonic couplant (Fannin UK Ltd.), prior to every scan to ensure good acoustic transmission. The transducer was moved along the length of each cell at a fixed pressure using an Olympus GLIDER 2-axis encoded scanner with the step size set at 1 mm to give a resolution of ca. 1 mm2. Due to the large size of the cells, the active aperture of the probe was wide enough to cover 1/3 the width, meaning that three measurements for each cell were taken and the data was combined to form the colour maps. Data from the ultrasonic signals were analysed using FocusPC software. The waveforms recorded by the transducer were exported and plotted using custom Python code to compare how the signal changes at different points in the cell. For consistency, a specific ToF range was selected for all cells, chosen because it is where the part of the waveform, known as the ‘echo-peak’, is located.74 The echo-peak is useful to monitor as it is where the waveform has travelled the whole way through the cell and reflected from the back surface, so characterising the entire cell. The maximum amplitude of the ultrasonic signal within this ToF range, at each point, are combined to produce a colour map. The signal amplitude is a percentage proportion of 100 where 100 is the maximum intensity of the signal, meaning that the signal has been attenuated the least as it travels through the cell, and 0 is the minimum intensity. The intensity is absolute and not normalised across all scans, meaning that an amplitude values on different cells can be directly compared. The Pristine cell is a second-generation Nissan Leaf pouch, different to the first-generation aged cells of varying orientation. The authors were not able to acquire an identical first-generation pristine Nissan Leaf cell. Nonetheless, it was expected that the Pristine cell would contain a uniform internal structure regardless of the specific chemistry and this would be identified in an ultrasound map consisting of a single colour (or narrow colour range).

The street maps 1 : 100,000 are published in cooperation with the Brandenburg State Office for Roads. Based on the regional maps, the information from the road database and other technical data are transferred. The maps contain all network node and road section numbers of the classified roads from the district road to the federal motorway. The geographical grid serves as an orientation grid, the Gauss-Krüger coordinates are marked in the map frame. The back contains a gazetteer with search index and community key. The street maps are published for each district.

A list of central and local government land in England, which may not be registered with HM Land Registry (HMLR).

HMLR has created this dataset for the Ministry for Housing, Communities and Local Government (MHCLG) by combining HMLR freehold polygon data with the public sector ownership data currently openly available from the Office of Government Property.

The dataset is not definitive or complete as not all central and local government data is captured, and/or available, and the two datasets are not held in the same format. The list is therefore indicative rather than definitive.

Intellectual Property Rights

The dataset includes address data processed against Ordnance Survey’s AddressBase Premium product and incorporates Royal Mail’s PAF® database (Address Data). Royal Mail and Ordnance Survey permit your use of Address Data:

• for personal and/or non-commercial use
• in relation to the analysis of public sector land and property.

If you want to use the Address Data in any other way, you must contact Royal Mail. Email

Address data

The following fields comprise the address data included in the dataset

• Property Name
• Street No
• Road
• Town
• Postcode

This dataset contains reconstructions of land use and land cover from AD 800 to 1992 in global coverage at 30 minute resolution. After AD 1700, the data is based on Ramankutty and Foley (1999), Foley et al. (2003) and Klein Goldewijk (2001); for earlier times, land use is estimated with a country-based method that uses national population data (McEvedy and Jones, 1978) as a proxy for agricultural activity. For each year, a map is provided that contains 14 fields. Each field holds the fraction the respective vegetation type covers in the total grid cell (0-1). The vegetation types comprise three human land use types (crop, C3 pasture and C4 pasture) and 11 natural vegetation types (based on the potential vegetation map of Ramankutty and Foley, 1999). For the time period prior to AD 1700 two additional land cover scenarios are provided (scenmin and scenmax). They quantify the uncertainties associated with this approach, through technological progress in agriculture and uncertainties in population estimates. The additional datasets combine the known uncertainties in a way to give the most extreme range for possible estimates of land use area for each year before 1700. The datasets thus do not represent consistent time series of plausible alternative scenarios, but indicate, for each year, a maximum range outside which estimates of land use area are unrealistic. See citations and references for details. Vegetation types: 1 Tropical evergreen forest 2 Tropical deciduous forest 3 Temperate evergreen broadleaf forest 4 Temperate/boreal deciduous broadleaf forest 5 Temperate/boreal evergreen conifers 6 Temperate/boreal deciduous conifers 7 Raingreen shrubs 8 Summergreen shrubs 9 C3 natural grasses 10 C4 natural grasses 11 Tundra 12 Crop 13 C3 pasture 14 C4 pasture

This dataset is comprised of boundary geometry for all of the incorporated municipalities and Northern Affairs Communities within the Province of Manitoba. The boundaries in this dataset represent the descriptions set forth in the Municipal Status and Boundaries Regulation (567/88R) and subsequent amendments, and regulations under The Northern Affairs Act. The geospatial referencing of the Municipal Boundaries is based on the best available land parcel data for each municipality and Northern Affairs Community. Where available, cadastral data based on registered survey plans was used. Otherwise, the Manitoba Property Assessment Information dataset produced by Manitoba Municipal Relations was used. The delineation of the boundaries was established according to the regulation establishing or amending the boundary of a municipality or Northern Affairs Community. Dataset content is subject to: Ongoing changes in municipal status and boundary and Northern Affairs Community boundary alterations that are generated through annexations, amalgamations, dissolutions or formations, all of which are approved by regulations made under The Municipal Act (for incorporated municipalities) and The Northern Affairs Act (for Northern Affairs Communities). This dataset content is current to the most recent effective date of any such regulation amendment. Ongoing development and maintenance of the land parcel datasets. Improvements to the cadastral data are used to improve the positional accuracy of the municipal boundary polygons. As of the publication date of this dataset the following known issues remain: For municipal boundaries and Northern Affairs Communities that include water boundaries, such as in the Lake Winnipeg and Lake Manitoba areas the water boundary portion that best reflects the municipal boundary was used. In some instances, road allowances on a municipal boundary are included in both of the adjoining municipalities. Please visit the Manitoba Municipal Relations website for more information www.gov.mb.ca/mr/land_use_dev/index.html. The Manitoba Municipal Boundaries data reflects the status of municipal boundaries and Northern Affairs Communities at the time of export and was uploaded to Manitoba Maps as a feature layer. Fields Included: OBJECTID: Sequential unique whole numbers that are automatically generated MUNI_NO: Manitoba municipality identifier number MUNI_NAME: Legal name of municipality MUNI_TYPE: Type of municipality MUNI_LIST_NAME: Name of municipality suitable for alphabetical list MUNI_LIST_NAME_WITH_TYPE: Name of municipality suitable for alphabetical list including type

This data was developed in response to citizens’ road maintenance requests from across the state as to whom to contact as the official maintenance authority - be it MDOT State Highway Administration, MDOT Transportation Authority, a county, or a municipality.MDOT SHA Website

Crack In The Mountain TrailDescription: This scenic trail passes through one of the most famous slot canyons on the Lower Colorado. It ends at a scenic cove on the lake beside Balance Rock, a huge rock mass precariously balanced atop a narrow column of stone. Big Horn Sheep are common!Directions: Go south on HWY 95 to McCulloch Blvd (mp 177). Turn right into SARA PARK and go ¾ mile to the trailhead on the right. Go through the gate and follow either trail (yellow or red) down to the canyon, which narrows to an arms width, tumbling down a series of dry falls through the crack. Except for a seven-foot dry fall, you can, with care, readily negotiate the drops. The seven-footer is smooth and can be slid down, like in a playground. It is possible to climb back up on the rocks alongside the waterfall. NOTE: (After a rain, water may block your passage.) The crack widens just before mile 1.4. Those not wishing to walk the additional mile to the lake can ascend the right bank on a trail which climbs to the upper route (blue trail) for the return. Otherwise, continue down the wash. When blocked by greenery, go up the trail on the right (blue), then cut left on the spur to the green trail over to the campsite at Balanced Rock cove (2.5 miles). The return is usually made all the way back on the upper route which meets the yellow and red trails.General Location: Lake Havasu City areaTrail Distance: 5 milesTrail Type: Non-motorizedDifficulty: ModerateTrail Use Guidelines:Please stay on the designated trail.Keep to the right of the trail, save the left for passing.All downhill traffic yields to uphill traffic.Approach each turn as if someone were around the corner.Keep pets under control and/or on a leash when on the trail.Leave no trace.Plan ahead and prepare.Dispose of waste properly.Leave what you find.Respect wildlife.Be considerate of other visitors.

Street right of way polygons are public spaces along the DC managed street segments. A database provided by DDOT identified ROW locations from authoritative source documentation including DDOT surface maps, ROW distribution cards, Subdivision Books in the DC Surveyor's Office, Survey Books, King Plats, Record Books, Street Extension Maps, County Maps, AT Map Books, Wall Books and Wall Reports.

Download In State Plane Projection Here.

The pavement boundaries were traced from aerial photography taken between March 15th, 2018 and April 25th, 2018. This dataset should meet National Map Accuracy Standards for a 1:1200 product. Lake County staff reviewed this dataset to ensure completeness and correct classification. In the case of a divided highway, the pavement on each side is captured separately. Island features in cul-de-sacs and in roads are included as a separate polygon.

TYPE is a code referring to the type of pavement.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each month going back to 1880. Be sure to configure the time settings in your web map to view the time series correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. A version showing just the most recent month is available here.