Exports in Puerto Rico increased to 5404.50 USD Million in May from 4158.50 USD Million in April of 2025. This dataset provides - Puerto Rico Exports - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical chart and dataset showing Puerto Rico exports by year from 1971 to 2023.

This Dataset contains year, month-wise export data of all commodities at HS level 2 for Puerto Rico. It has other details like quantity of export, value of export in Indian Rupees and US Dollars

Exports to Puerto Rico in Mexico increased to 61651 USD Thousand in January from 57050 USD Thousand in December of 2023. This dataset includes a chart with historical data for Mexico Exports to Puerto Rico.

This Dataset contains year-wise import and export data related to each HS code for Puerto Rico

This table contains 345 series, with data for years 1968 - 1992 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Trading area (2 items: Union of Soviet Socialist Republics (U.S.S.R.); Puerto Rico) Summary export groups (SEG) and other aggregations (185 items: Total exports; Total re-exports; Total domestic exports; Total live animals; ...). 

Exports to Puerto Rico in Australia remained unchanged at 0 AUD Million in February. This dataset includes a chart with historical data for Australia Exports to Puerto Rico.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• name
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer

This dataset is one of many "/dataset?tags=openstreetmap">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] IS NOT NULL OR tags['man_made'] IS NOT NULL OR tags['shop'] IS NOT NULL OR tags['tourism'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• amenity
• man_made
• shop
• tourism
• opening_hours
• beds
• rooms
• addr:full
• addr:housenumber
• addr:street
• addr:city
• source
• name:es

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')

Features may have these attributes:

• name
• name:en
• amenity
• operator
• network
• addr:full
• addr:city
• source
• name:es

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['railway'] IN ('rail','station')

Features may have these attributes:

• name
• name:en
• railway
• ele
• operator:type
• layer
• addr:full
• addr:city
• source
• name:es

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay')

Features may have these attributes:

• name
• waterway
• covered
• width
• depth
• layer
• blockage
• tunnel
• natural
• water

This dataset is one of many "/dataset?tags=openstreetmap">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This page displays a table with Puerto Rico Exports By Country in U.S. dollars, according to the United Nations COMTRADE database on international trade.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university')

Features may have these attributes:

• name
• name:en
• amenity
• building
• operator:type
• capacity:persons
• addr:full
• addr:city
• source
• name:es

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This table contains 56 series, with data for years 1988 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Trade (2 items: Import;Export ...), Basis (2 items: Customs;Balance of payments ...), Seasonal adjustment (2 items: Unadjusted;Seasonally adjusted ...), Principal trading areas (7 items: Total of all merchandise;United States; including Puerto Rico and Virgin Islands;European Union excluding the United Kingdom;United Kingdom ...).

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• amenity
• building
• port
• operator:type
• addr:full
• addr:city
• source
• name:es

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• highway
• width
• name
• lanes
• smoothness
• surface
• layer
• source
• bridge
• oneway

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.