Graph and download economic data for Producer Price Index by Industry: Material Recyclers: No 1 Copper Wire, Heavy (PCU429930429930211) from Dec 1986 to Sep 2025 about heavy weight, wired, copper, materials, metals, PPI, industry, inflation, price index, indexes, price, and USA.

URL: https://geoscience.data.qld.gov.au/dataset/cr055291

GSQ PUBLICATION 135, INDEX (NO. 1) TO NAMES OF PLACES, MINES, REEFS, ETC, OCCURRING IN GEOLOGICAL SURVEY REPORTS, FROM NO. 1 TO NO. 134, INCLUSIVE

United States - Producer Price Index by Industry: Material Recyclers: No 1 Copper Wire, Heavy was 539.55900 Index Dec 1986=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Material Recyclers: No 1 Copper Wire, Heavy reached a record high of 627.90400 in May of 2024 and a record low of 87.10000 in November of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Material Recyclers: No 1 Copper Wire, Heavy - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Producer Price Index by Commodity: Intermediate Demand by Production Flow: Stage 1 Intermediate Demand (WPUID51) from Nov 2009 to Sep 2025 about intermediate, flow, production, commodities, PPI, inflation, price index, indexes, price, and USA.

United States - Producer Price Index by Commodity: Metals and Metal Products: No. 1 Copper Scrap, Including Wire was 546.41000 Index Dec 1986=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Metals and Metal Products: No. 1 Copper Scrap, Including Wire reached a record high of 599.28500 in April of 2022 and a record low of 87.10000 in November of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Metals and Metal Products: No. 1 Copper Scrap, Including Wire - last updated from the United States Federal Reserve on November of 2025.

Norway NO: Human Capital Index (HCI): Scale 0-1 data was reported at 0.771 NA in 2017. Norway NO: Human Capital Index (HCI): Scale 0-1 data is updated yearly, averaging 0.771 NA from Dec 2017 (Median) to 2017, with 1 observations. Norway NO: Human Capital Index (HCI): Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Human Capital Index. The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Norway NO: Logistics Performance Index: 1=Low To 5=High: Overall data was reported at 3.732 NA in 2016. This records a decrease from the previous number of 3.958 NA for 2014. Norway NO: Logistics Performance Index: 1=Low To 5=High: Overall data is updated yearly, averaging 3.810 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 3.958 NA in 2014 and a record low of 3.680 NA in 2012. Norway NO: Logistics Performance Index: 1=Low To 5=High: Overall data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Transportation. Logistics Performance Index overall score reflects perceptions of a country's logistics based on efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010).; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Norway NO: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data was reported at 3.616 NA in 2016. This records an increase from the previous number of 3.423 NA for 2014. Norway NO: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data is updated yearly, averaging 3.490 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 3.620 NA in 2007 and a record low of 3.350 NA in 2010. Norway NO: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Description: LiDAR derived canopy structure and topography across SAFE, Maliau Conservation Area and Danum Valley in Malaysian Borneo. These maps were produced following a survey by the Natural Environment Research Council Airborne Research Facility in 2014. Georeferenced point clouds were tiled, noise points were removed and ground points classified using Lastools. Digital terrain models (DTM) were produced from classified ground points at 10 m resolution. Point clouds were normalised (through ground subtraction) to produce 1 m resolution pitfree canopy height model (CHM) rasters. Normalised CHMs were also used to produce 20 m resolution plant area density (PAD) profile and plant area index (PAI) rasters as well as a number of statistics calculated for 20 m resolution vertical profiles. Point density is reported as a means to assess data quality, higher values indicate more data and are likely to be more reliable, particularly for dense tall forests, which depend on high point densities for accurate ground detection. Above-ground carbon density was calculated at 1 ha resolution from top of canopy height and gap fraction (derived from canopy height models) also at 1 ha resolution. Project: This dataset was collected as part of the following SAFE research project: Influences of disturbance and environmental variation on biomass change in Malaysian Borneo Funding: These data were collected as part of research funded by:

Natural Environmental Research Council (Human Modified Tropical Forests Consortium Grant, NE/K016377/1) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following authorities:

Sabah Biodiversity Council (Research licence Unknown)

XML metadata: GEMINI compliant metadata for this dataset is available here Files: This dataset consists of 64 files: SAFE_archive_LiDAR_Swinfield.xlsx, Danum_acd.tif, Danum_chm.tif, Danum_dtm.tif, Danum_pad_canopy_height.tif, Danum_pad_kurt.tif, Danum_pad_mean.tif, Danum_pad_n_layers.tif, Danum_pad_shannon.tif, Danum_pad_shape.tif, Danum_pad_skew.tif, Danum_pad_std.tif, Danum_pai.tif, Danum_pai_02_10m.tif, Danum_pai_10_20m.tif, Danum_pai_20_30m.tif, Danum_pai_30_40m.tif, Danum_pai_40_50m.tif, Danum_pai_50_60m.tif, Danum_pai_60_70m.tif, Danum_pai_70_80m.tif, Danum_point_density.tif, Maliau_acd.tif, Maliau_chm.tif, Maliau_dtm.tif, Maliau_pad_n_layers.tif, Maliau_pad_canopy_height.tif, Maliau_pad_kurt.tif, Maliau_pad_mean.tif, Maliau_pad_shannon.tif, Maliau_pad_shape.tif, Maliau_pad_skew.tif, Maliau_pad_std.tif, Maliau_pai.tif, Maliau_pai_02_10m.tif, Maliau_pai_10_20m.tif, Maliau_pai_20_30m.tif, Maliau_pai_30_40m.tif, Maliau_pai_40_50m.tif, Maliau_pai_50_60m.tif, Maliau_pai_60_70m.tif, Maliau_pai_70_80m.tif, Maliau_point_density.tif, SAFE_acd.tif, SAFE_pad_canopy_height.tif, SAFE_chm.tif, SAFE_dtm.tif, SAFE_pad_kurt.tif, SAFE_pad_mean.tif, SAFE_pad_n_layers.tif, SAFE_pad_shannon.tif, SAFE_pad_shape.tif, SAFE_pad_skew.tif, SAFE_pad_std.tif, SAFE_pai.tif, SAFE_pai_02_10m.tif, SAFE_pai_10_20m.tif, SAFE_pai_20_30m.tif, SAFE_pai_30_40m.tif, SAFE_pai_40_50m.tif, SAFE_pai_50_60m.tif, SAFE_pai_60_70m.tif, SAFE_pai_70_80m.tif, SAFE_point_density.tif SAFE_archive_LiDAR_Swinfield.xlsx This file only contains metadata for the files below Danum_acd.tif Description: Danum Valley above-ground carbon density This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_acd) Description: Danum Valley above-ground carbon density Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_acd: Above-ground carbon density (ACD) (Field type: numeric)

Danum_chm.tif Description: Danum Valley canopy height model This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_chm) Description: Danum Valley canopy height model Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_chm: Canopy height model (CHM) (Field type: numeric)

Danum_dtm.tif Description: Danum Valley digital terrain model This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_dtm) Description: Danum Valley digital terrain model Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_dtm: Digital terrain model (DTM) (Field type: numeric)

Danum_pad_canopy_height.tif Description: Danum Valley maximum canopy height This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_canopy_height) Description: Danum Valley maximum canopy height Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_canopy_height: Maximum canopy height (Field type: numeric)

Danum_pad_kurt.tif Description: Danum Valley plant area density kurtosis This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_kurt) Description: Danum Valley plant area density kurtosis Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_kurt: Plant area density kurtosis (Field type: numeric)

Danum_pad_mean.tif Description: Danum Valley plant area density mean This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_mean) Description: Danum Valley plant area density mean Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_mean: Plant area density central height (Field type: numeric)

Danum_pad_n_layers.tif Description: Danum Valley number of discrete plant area density layers This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_n_layers) Description: Danum Valley number of discrete plant area density layers Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_n_layers: Plant area density number of layers (Field type: numeric)

Danum_pad_shannon.tif Description: Danum Valley plant area density shannon index This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_shannon) Description: Danum Valley plant area density shannon index Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_shannon: Plant area density Shannon index (Field type: numeric)

Danum_pad_shape.tif Description: Danum Valley plant area density shape This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_shape) Description: Danum Valley plant area density shape Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_shape: Plant area density shape (Field type: numeric)

Danum_pad_skew.tif Description: Danum Valley plant area density skew This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_skew) Description: Danum Valley plant area density skew Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_skew: Plant area density skew (Field type: numeric)

Danum_pad_std.tif Description: Danum Valley plant area density standard deviation This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pad_std) Description: Danum Valley plant area density standard deviation Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pad_std: Plant area density standard deviation (Field type: numeric)

Danum_pai.tif Description: Danum Valley plant area index This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai) Description: Danum Valley plant area index Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai: Plant area index (Field type: numeric)

Danum_pai_02_10m.tif Description: Danum Valley plant area index between 2 m and 10 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_02_10m) Description: Danum Valley plant area index between 2 m and 10 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_02_10m: Plant area index between 2 m and 10 m above ground (Field type: numeric)

Danum_pai_10_20m.tif Description: Danum Valley plant area index between 10 m and 20 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_10_20m) Description: Danum Valley plant area index between 10 m and 20 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_10_20m: Plant area index between 10 m and 20 m above ground (Field type: numeric)

Danum_pai_20_30m.tif Description: Danum Valley plant area index between 20 m and 30 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_20_30m) Description: Danum Valley plant area index between 20 m and 30 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_20_30m: Plant area index between 20 m and 30 m above ground (Field type: numeric)

Danum_pai_30_40m.tif Description: Danum Valley plant area index between 30 m and 40 m above ground This file contains 1 data tables:

Attribute table for raster (described in worksheet Danum_pai_30_40m) Description: Danum Valley plant area index between 30 m and 40 m above ground Number of fields: 1 Number of data rows: Unavailable (table metadata description only). Fields:

Danum_pai_30_40m: Plant

Vat blue 1 (synthetic indigo),"Colour Index No. 73000"

The CMP Objective Measures GIS layer includes scoring by road segment where congested road segment locations that meet CMP Objective Measure criteria than others contain higher score totals and are given stronger consideration for managing congestion. The final mapping is a composite of all the scores where a maximum score of 15 can be attained, and road segment locations with scores greater than nine are shown in brown.The CMP Objective Measures include: 1) increase mobility and reliability; 2) integrate modes and increase accessibility; 3) modernize infrastructure; 4) achieve Vision Zero; 5) make global connections; 6) strengthen security and enhance emergency preparedness; and 7) support Long-Range Plan principles.CMP Objective Measure Database FieldsSegID – INRIX Segment IDFRC – INRIX facility ID as applicableMiles – INRIX segment milesRoadNumber – INRIX Road Highway numberRoadName – INRIX Road nameState - INRIX StateCounty – INRIX CountyBearing – INRIXD BearingSEGID2X – INRIX Segment ID (string)TTIWKD0708 – INRIX Travel Time Index 7-8 amTTIWKD0809 – INRIX Travel Time Index 8-9 amTTIWKD1617 – INRIX Travel Time Index 4-5 pmTTIWKD1718 – INRIX Travel Time Index 5-6 pmPTIWKD0708 – INRIX Planning Time Index 7-8 amPTIWKD0809 – INRIX Planning Time Index 8-9 amPTIWKD1617 – INRIX Planning Time Index 4-5 pmPTIWKD1718 – INRIX Planning Time Index 5-6 pmREFSPDMEAN – Reference or Freeflow speedFFTIME – Freeflow TimeMAJTMC – Primary INRIX TMC associated with INRIX XDNHS – Roadway segment on NHS based on NPMRDS database Valid values: 1 – On NHS 2 – Not on NHSLOTTRMAX – Roadway segment LOTTR value TTTRMAX – Roadway segment TTTRMAX value PHEDVAL – Roadway segment PHEDVAL value from conflated TMC PHEDVALPMI – Roadway segment PHEDVAL value Per Mile of Roadway from conflated TMC FROMTONODE – Travel Demand Model From To Node LinkVC7to8am15 – Travel Demand Model V/C 7 am to 8 am 2015VC8to9am15 – Travel Demand Model V/C 8 am to 9 am 2015VC4to5pm15 – Travel Demand Model V/C 4 am to 5 pm 2015VC5to6pm15 – Travel Demand Model V/C 5 am to 6 pm 2015VCMaxPH15 – Highest of 2015 am and pm V/CsVC7to8am50 – Travel Demand Model V/C 7 am to 8 am 2050VC8to9am50 – Travel Demand Model V/C 8 am to 9 am 2050VC4to5pm50 – Travel Demand Model V/C 4 am to 5 pm 2050VC5to6pm50 – Travel Demand Model V/C 5 am to 6 pm 2050VCMaxPH50 – Highest of 2050 am and pm V/CsPCPH7to8AM – Percent Change in V/C from 2015 to 2050 during 7-8amPCPH8to9AM – Percent Change in V/C from 2015 to 2050 during 8-9amPCPH4to5PM – Percent Change in V/C from 2015 to 2050 during 4-5pmPCPH5to6PM – Percent Change in V/C from 2015 to 2050 during 5-6pmPCMaxPH – Highest of Percent Change V/CTRANSIT – Travel Demand Model road segments that include surface transit (bus or trolley) Valid values: 0 – No road segments with transit 1 – Road segments with any transit 2 – Road segments with substantial transit (2 or more in urban; 3 or more suburban)rmshash – PennDOT road segments county, route and segment identifiers from PennDOTs RMSSEG databasenewaadtv2 – Average Annual Daily Traffic (AADT)newadttv2 – Average Annual Daily Truck Traffic (AADTT)DVRPCCR_RT – Average crash rate analyzed separately for the PA and NJ portions of DVRPC region based on similar roadway characteristicsSEG_CR_RT – Actual crash rate by roadway segmentCR_INDEX - Crash Rate index comparing actual crash rate with average rate (analyzed separately for the PA and NJ portions of DVRPC region)FATALRATE – Fatal crash rate based on fatalities per 100,000,000 million vehicle miles traveledCR_INDEX_C – Crash Rate Valid values: 0 – Crash rate less than 4 times the state rate 1 – Crash rate 4 or more times the state rateCRASH_CT – Number of crashes by roadway segmentFATAL_CT – Number of fatal crashes by roadway segmentTOT_INJ_CT – Number of injury crashes by roadway segmentMAJ_INJ_CT – Number of major injury crashes by roadway segmentMOD_INJ_CT – Number of moderate injuries by roadway segmentMIN_INJ_CT – Number of minor injuries by roadway segmentUNK_INJ_CT - Unknown injuries by roadway segmentCRASHPERMI – Crashes per MileFATMAJINJC – Fatal and major injuries by roadway segment FATMIPMILE – Fatalities and Major Injuries Per MileFATMAPMICR – Fatalities and Major Injuries Per Mile Crash Index Valid values: 0 – Less Than 5 or more fatal and major injuries per roadway segment mile 1 – 5 or more fatal and major injuries per roadway segment mileTTTIMAXVAL – Highest Truck Travel Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pmFACIDNEW – Focus Roadway Corridor Facility identifierFACIDDIR – Focus Roadway Corridor Facility direction, which is used to calculate AADTTTICR - Roadway segment congestion threshold – TTICRP Valid values: 0 – TTI value Less Than 1.20 1 – TTI value >= 1.20 and <= 1.50 (moderately congested) 2 – TTI > 1.50 (highly congested)TTIWKDMAX - Highest of INRIX Travel Time Index 7-8am, 8-9am, 4-5pm, 5-6pmPTIWKDMAX - Highest of INRIX Planning Time Index 7-8am, 8-9am, 4-5pm, 5-6pmPTICR - Roadway segment reliability threshold - PTICRP Valid values: 0 – PTI value Less Than 2.00 1 – PTI value >= 2.00 and =< 3.00 (moderately unreliable) 2 – PTI > 3.00 and <= 3.50 (highly unreliable) 3 – PTI > 3.50 (very highly unreliable)LOTTRCR – Roadway segment reliable threshold Valid values: 0 – LOTTR < 0 1.50; reliable 1 – LOTTR 1.50 to 2.00; moderately unreliable 2 – LOTTR > 2.00; highly unreliable TTTRCR – Roadway segment reliable threshold (trucks on interstates only) Valid values: 0 – TTTR value is 0 1 – TTTRMAX > 0 and Less Than 1.50; reliable 2 – TTTRMAX 1.50 to 2.49; moderately unreliable 3 – TTTRMAX >= 2.50; highly unreliablePHEDCR – Roadway segment that experiences excessive delay above the regional average (24,355 PHED/Mile) (based on NPMRDS database, not on conflated INRIX database). Valid values: 0 – Not above regional average 1 – At or above regional averageVC8550PCR – Travel Demand Model Segments with >=15% change from 2015 to 2050 Valid values: 0 – Segments < 15% Change in V/C 1 – Segments >= 15% Change in V/CVC8550CR – Travel Demand Model Road segments in 2050 with 0.85 or more V/C Valid values: 0 – Segments < 0.85 V/C 1 – Segments >= 0.85 V/CTTTICR – Truck Travel Time Index Roadway segment truck congestion threshold Valid values: 0 – Less Than 2.00 1 – 2.00 to 3.00 (moderately congested) 2 - > 3.00 (highly congested)SETRANSTCR – Transit Score Priority Valid values: 0 – Not Priority 1 – PriorityCRSSRIID – NJ Crash Identifier SRI popdenmnx2 – Road segments that intersect CBGs with population more than 2x the regional average Valid values: 0 –No 1 – Yesempdenmnx2 – Road segments that intersect CBGs with employment more than 2x the regional average Valid values: 0 –No 1 – Yesrailsta1mi – Road segments that intersect 1 mile buffer of rail stations Valid values: 0 –No 1 – Yestrnstqtrmi – Road segments that intersect 1/4 mile buffer of bus transit routes Valid values: 0 –No 1 – Yesraillin1mi – Road segments that intersect 1 mile buffer of passenger rail Valid values: 0 –No 1 – Yesfrtrail1mi – Road segments that intersect 1 mile buffer of freight rail Valid values: 0 –No 1 – Yesfrtcntr1mi - Road segments that intersect freight centers Valid values: 0 –No 1 – Yestrnstscor - Road segments that intersect CBG with very and high composite (pop, emp and 0 veh households) Valid values: 0 –No 1 – YesTPTIMAXVAL – Highest Truck Planning Time Index value for 7-8am, 8-9am, 4-5pm and 5-6pm.TPTICR – Truck Planning Time Index Roadway segment truck reliability threshold Valid values: 0 – Less Than 2.00 1 – 5.50 to 6.50 (moderately unreliable) 2 - > 6.50 (highly unreliable)HVTRST1mi – Heavily used transit stations: 3 in Philadelphia and 1 in each of the other PA counties; road segments that intersect within 1-mile buffer Valid values: 0 –No 1 – YesNUCPLT1MI – Road segments within 10 mile buffer of Limerick Nuclear Power Plant Valid values: 0 –No 1 – YesRDBRGR1MI – Road segments within 1 mile buffer of major road bridges carrying > 100,000 AADT Valid values: 0 –No 1 – YesFRBRGR1MI – Road segments within 1 mile buffer of major freight bridges Valid values: 0 –No 1 – YesRLBRGR1MI – Road segments within 1 mile buffer of major passenger rail bridges Valid values: 0 –No 1 – YesMIL1MI – Road segments within 1 mile buffer of major military sites Valid values: 0 –No 1 – YesSTDWTF1MI – Road segments within 1 mile buffer of major military sites Valid values: 0 –No 1 – Yeslucntr – Road segments that intersect 2050 land use centers Valid values: 0 –No 1 – YesIREGAreas – Road segments that intersect Infill, Redevelopment and Emergency Growth Areas Valid values: 0 –No 1 – YesENSCAreas – Road segments where 90% or more of segment score low in the Environmental Screening Tool (or not environmental sensitive areas) Valid values: 0 –No 1 – YesFLD100500 – Road segments that intersect 100-year and 500-year floodplain Valid values: 0 –No 1 – YesIPDEJLI – Road segments that intersect IPD CBG’s with well above average or above average low income populations Valid values: 0 –No 1 – YesIPDEJRM – Road segments that intersect IPD CBG’s with well above average or above average racial minority populations Valid values: 0 –No 1 – YesTTICRP – Travel Time Index criteria PointsPTICRP – Planning Time Index criteria PointsPHEDCRP – PHED criteria PointsVC8550CRP – high anticipated V/C criteria PointsVC8550PCRP – high anticipated growth in V/C criteria PointsLOTTRCRP – LOTTR criteria PointsIMRMAXP – increase mobility and reliability maximum PointstrnstscorP – transit score criteria Pointsrailsta1mP – near

No description is available. Visit https://dataone.org/datasets/f433bb869ddad66c0958548ca5ea5796 for complete metadata about this dataset.

Graph and download economic data for Producer Price Index by Commodity: Metals and Metal Products: No. 1 Copper Scrap, Including Wire (WPU10230101) from Dec 1986 to Sep 2025 about wired, copper, metals, commodities, PPI, inflation, price index, indexes, price, and USA.

Goal 1: End poverty in all its forms everywhereGlobally, the number of people living in extreme poverty has declined by more than half from 1.9 billion in 1990. However, 836 million people still live in extreme poverty. About one in five persons in developing regions lives on less than $1.25 per day.Southern Asia and sub-Saharan Africa are home to the overwhelming majority of people living in extreme poverty.High poverty rates are often found in small, fragile and conflict-affected countries.One in four children under age five in the world has inadequate height for his or her age.The all India Poverty Head Count Ratio (PHCR) has been brought down from 47% in 1990 to 21% in 2011-2012, nearly halved.This map layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

Monthly indexes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the current month and previous four months. The base year for the index is 2002=100.

Since 2016, the global edition of the Sustainable Development Report (SDR) has provided the most up-to-date data to track and rank the performance of all UN member states on the SDGs. This year’s edition was written by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN. It focuses on the UN Summit of the Future, with an opening chapter endorsed by 100+ global scientists and practitioners. The report also includes two thematic chapters, related to SDG 17 (Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development) and SDG 2 (End hunger, achieve food security and improved nutrition and promote sustainable agriculture).This year’s SDR highlights five key findings:On average, globally, only 16% of the SDG targets are on track to be achieved by 2030, with the remaining 84% demonstrating limited or a reversal of progress. At the global level, SDG progress has been stagnant since 2020, with SDG 2 (Zero Hunger), SDG11 (Sustainable Cities and Communities), SDG14 (Life Below Water), SDG15 (Life on Land) and SDG16 (Peace, Justice, and Strong Institutions) particularly off-track. Globally, the five SDG targets on which the highest proportion of countries show a reversal of progress since 2015 include: obesity rate (under SDG 2), press freedom (under SDG 16), the red list index (under SDG 15), sustainable nitrogen management (under SDG 2), and – due in a large part to the COVID-19 pandemic and other factors that may vary across countries – life expectancy at birth (under SDG 3). Goals and targets related to basic access to infrastructure and services, including SDG9 (Industry, Innovation, and Infrastructure), show slightly more positive trends, although progress remains too slow and uneven across countries.The pace of SDG progress varies significantly across country groups. Nordic countries continue to lead on SDG achievement, with BRICS demonstrating strong progress and poor and vulnerable nations lagging far behind. Similar to past years, European countries – notably Nordic countries – top the 2024 SDG Index. Finland ranks number 1 on the SDG Index, followed by Sweden (#2), Denmark (#3), Germany (#4), and France (#5). Yet, even these countries face significant challenges in achieving several SDGs. Average SDG progress in BRICS (Brazil, the Russian Federation, India, China, and South Africa) and BRICS+ (Egypt, Ethiopia, Iran, Saudi Arabia, and the United Arab Emirates) since 2015 has been faster than the world average. In addition, East and South Asia has emerged as the region that has made the most SDG progress since 2015. By contrast, the gap between the world average SDG Index and the performance of the poorest and most vulnerable countries, including Small Island Developing States (SIDS), has widened since 2015.Sustainable development remains a long-term investment challenge. Reforming the Global Financial Architecture is more urgent than ever. The world requires many essential public goods that far transcend the nation-state. Low-income countries (LICs) and lower-middle-income countries (LMICs) urgently need to gain access to affordable long-term capital so that they can invest at scale to achieve their sustainable development objectives. Mobilizing the necessary levels of finance will require new institutions, new forms of global financing — including global taxation —, and new priorities for global financing, such as investing in quality education for all. The report presents five complementary strategies to reform the Global Financial Architecture.Global challenges require global cooperation. Barbados ranks the highest in its commitment to UN-based multilateralism; the United States ranks last. As with the challenge of SDGs, strengthening multilateralism requires metrics and monitoring. The report’s new Index of countries’ support to UN-based multilateralism (UN-Mi) ranks countries based on their engagement with the UN system including treaty ratification, votes at the UN General Assembly, membership in UN organizations, participation in conflicts and militarization, use of unilateral sanctions and financial contributions to the UN. The five countries most committed to UN-based multilateralism are: Barbados (#1), Antigua and Barbuda (#2), Uruguay (#3), Mauritius (#4), and the Maldives (#5). By contrast, the United States (#193), Somalia (#192), South Sudan (#191), Israel (#190), and the Democratic Republic of Korea (#189) rank the lowest on the UN-Mi.SDG targets related to food and land systems are particularly off-track. The SDR presents new FABLE pathways to support sustainable food and land systems. Globally, 600 million people will still suffer from hunger by 2030, obesity is increasing globally, and greenhouse gas emissions from Agriculture, Forestry, and Other Land Use (AFOLU) represent almost a quarter of annual global GHG emissions. The new FABLE pathways brought together more than 80 local researchers across 22 countries to assess how 16 targets related to food security, climate mitigation, biodiversity conservation, and water quality could be achieved by 2030 and 2050. The continuation of current trends widens the gap with targets related to climate mitigation, biodiversity, and water quality. Pursuing commitments that have been already taken by countries would improve the situation, but they are still largely insufficient. Significant progress is possible but requires several dramatic changes: 1) avoid overconsumption beyond recommended levels and limit animal-based protein consumption with dietary shifts compatible with cultural preferences; 2) invest to foster productivity, particularly for products and areas with strong demand growth; and 3) implement inclusive, robust, and transparent monitoring systems to halt deforestation. Our sustainable pathway avoids up to 100 million hectares of deforestation by 2030 and 100 Gt CO2 emissions by 2050. Additional measures would be needed to avoid trade-offs with on-farm employment and water pollution due to excessive fertilizer application and ensure that no one is left behind, particularly to end hunger.About the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailGrayson FullerManager, SDG Index & Data team, SDSNGrayson Fuller is the manager of the SDG Index and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: EmailAbout the PublishersDublin University PressDublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

The dataset shows index numbers of infrastructure industries

Note: 1. Weights represent weight of Index Number of Industrial Production. 2. Refinery Products has 93 percent of the crude throughout 3. Refinery Products’ yearly growth rate of 2012-13 is not comparable with other years on account of inclusion of RIL (SEZ) production data since April, 2012

Timeseries data from 'NORTHEAST SHARK RV SLOUGH NO. 1 NR COOPERTOWN, FL (USGS 254130080380500)' (gov_usgs_nwis_254130080380500)

Norway NO: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data was reported at 3.954 NA in 2016. This records a decrease from the previous number of 4.192 NA for 2014. Norway NO: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data is updated yearly, averaging 3.954 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 4.220 NA in 2010 and a record low of 3.820 NA in 2007. Norway NO: Logistics Performance Index: 1=Low To 5=High: Quality of Trade and Transport-Related Infrastructure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;