Interactive visualization of tariff rates imposed by the United States on countries around the world

Transport and Infrasutcture Parking meters for Dublin City. Includes location, code, No of spaces per street (PD-Pay and Display D Disc Parking), exact location, data install, tariff (cost per hour), nearest location of pay and display, clearway, if clearway conditions in operation (No parking or stopping during the hours indicated on the street sign), further information, finished, x coordinate, y coordinate, tariff zone (see map) and Parking Voucher outlets and locations Spatial projection: IG

Parking Meters location tariffs and zones in DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).Transport and Infrasutcture Parking meters for Dublin City. Includes location, code, No of spaces per street (PD-Pay and Display D Disc Parking), exact location, data install, tariff (cost per hour), nearest location of pay and display, clearway, if clearway conditions in operation (No parking or stopping during the hours indicated on the street sign), further information, finished, x coordinate, y coordinate, tariff zone (see map) and Parking Voucher outlets and locations Spatial projection: IG...

Data show fault-based seismic sources used in the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), which provides authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes in California. Fault model slip rates are given in millimeters per year. The feature service depicts the surface traces of modeled faults, which are simplified from the CGS – USGS Quaternary Fault and Fold database (https://earthquake.usgs.gov/hazards/qfaults/). For modeled blind fault seismic sources, the traces represent the map-view fault tip projection of the subsurface fault. For additional information regarding modeled faults in UCERF3 please refer to Appendix A of the UCERF3 report (https://pubs.usgs.gov/of/2013/1165/).

For additional information about UCERF3 please see https://www.conservation.ca.gov/cgs/rghm/psha/Pages/sr_228.aspx for the full UCERF3 publication and supporting products.

California Sales and Use Tax Rates Map - Current effective rates by jurisdiction

174 Active Global Tariff Of buyers list and Global Tariff Of importers directory compiled from actual Global import shipments of Tariff Of.

Discover how Volkswagen is addressing U.S. tariff challenges by considering strategic shifts in production and focusing on cost efficiencies to enhance competitiveness and future growth.

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Tariff Structure announced by Government of Pakistan.

7298 Global import shipment records of Preferential Tariff with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset includes public (common carrier) tariff rates for fertilizer shipments on 26 major rail corridors. Fertilizer commodities include potash, urea, urea ammonium nitrate (UAN), monoammonium phosphate (MAP), and diammonium phosphate (DAP). The dataset provides tariff rates and fuel surcharges for two car ownership types (privately-owned and railroad-owned railcars) and three train types (manifest, unit, and shuttle).

Estimating groundwater recharge rates is vitally important to understanding and managing groundwater. Numerous studies have used collated recharge datasets to understand and project regional or global-scale recharge rates. However, a key challenge stems from the inherent variability in recharge estimation methods utilised across these collations. Recharge estimation methods each carry distinct assumptions, address different recharge components, and operate over varied temporal scales. To address these challenges, this study uses a comprehensive dataset of over 200,000 groundwater chloride measurements to estimate groundwater recharge rates using the chloride mass balance (CMB) method throughout Australia. Recharge rates were produced stochastically using the groundwater chloride dataset and supplemented by gridded chloride deposition, runoff, and precipitation datasets within a Python framework. After QA/QC and data filtering, the resulting recharge rates and 17 spatial datasets are integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of recharge rates across Australia. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation, and the spatial recharge models collectively provide valuable insights for water resources management across the vast and dry Australian continent and similar approaches can be applied globally. If you use the datasets, gridded map output files, or Python scripts, we would appreciate it if you could cite the associated publication in Hydrology and Earth System Sciences here: https://hess.copernicus.org/articles/28/1771/2024/. For any further information, please do not hesitate to contact Stephen Lee on stephen.lee@cdu.edu.au.

• Description: Parking tariff areas * Source: management system municipality of The Hague * Purpose: Publication on the internet * Restrictions: This dataset is not suitable for legal advice * **Possibilities: ** This dataset is suitable for providing insight into the location on the map * Viewer: https://arcg.is/OqHC8 * Coordinate system: RDnew. The KML is WGS84

Breton Island, Louisiana Transects with Shoreline Change Rates (Pre/Post Hurricane Katrina) (Geographic, NAD83) consists of vector transect data that were derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

Beach erosion is a chronic problem along most open-ocean shores of the United States. As coastal populations continue to grow, and community infrastructures are threatened by erosion, there is increased demand for accurate information regarding past and present shoreline changes. There is also need for a comprehensive analysis of shoreline movement that is regionally consistent. To meet these national needs, the USGS National Assessment of Shoreline Change Project has collected and analyzed a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. This dataset consists of long-term (100+ years) shoreline change rates. Rate calculations were computed using the Digital Shoreline Analysis System (DSAS), an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate based on all available shoreline data. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates. To make these results more accessible to the public and other agencies, the USGS created this web service. This web service was created utilizing Esri ArcServer. This service meets open geospatial consortium standards. The data compilation used to derive the shoreline change rates is available in a service with the title USGS Map service: National Shoreline Change - Historic Shorelines by State. The reference baseline used to derive the shoreline change rates is available in a service with the title USGS Map service: National Shoreline Change - Offshore Baseline. The locations of the transects used in the change rate calculation are available in a service with the title USGS Map service: National Shoreline Change - Intersection Points. The geographic information system (GIS) data layers from this web service are cataloged by state for ease of access.

11017 Global import shipment records of Tariff Of with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population