Nothing
tests/testthat/soc_discovery/api.us.socrata.com/api/catalog/v1-9cfe35.R
In socratadata: Explore Socrata Data with Ease
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body = charToRaw("{\n \"results\" :\n [\n {\n \"resource\" :\n {\n \"name\" : \"Vital Signs: Commute Mode Choice (by Place of Residence) – Bay Area\",\n \"id\" : \"9mau-as85\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"VITAL SIGNS INDICATOR\\nCommute Mode Choice (T1)\\n\\nFULL MEASURE NAME\\nCommute mode share by residential location\\n\\nLAST UPDATED\\nApril 2020\\n\\nDESCRIPTION\\nCommute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.\\n\\nDATA SOURCE\\nU.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census\\nhttp://www.bayareacensus.ca.gov/transportation/Means19802000.htm\\n\\nU.S. Census Bureau: American Community Survey\\nForm B08301 (2006-2018; place of residence)\\nwww.api.census.gov\\n\\nCONTACT INFORMATION\\nvitalsigns.info@bayareametro.gov\\n\\nMETHODOLOGY NOTES (across all datasets for this indicator)\\nFor the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. \\\"Other\\\" includes bicycle, motorcycle, taxi, and other modes of transportation.\\n\\nFor the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode shares are population-weighted averages of the nine counties’ modal shares. \\\"Auto\\\" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. “Transit” includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). \\\"Other\\\" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.\\n\\nThe metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.\",\n \"attribution\" : \"U.S. Census Bureau\",\n \"attribution_link\" : \"https://data.census.gov\",\n \"contact_email\" : null,\n \"type\" : \"dataset\",\n \"updatedAt\" : \"2020-05-20T21:50:47.000Z\",\n \"createdAt\" : \"2020-04-09T17:01:10.000Z\",\n \"metadata_updated_at\" : \"2020-05-20T21:50:47.000Z\",\n \"data_updated_at\" : \"2020-04-15T20:55:37.000Z\",\n \"page_views\" :\n {\n \"page_views_last_week\" : 4,\n \"page_views_last_month\" : 9,\n \"page_views_total\" : 467,\n \"page_views_last_week_log\" : 2.321928094887362,\n \"page_views_last_month_log\" : 3.3219280948873626,\n \"page_views_total_log\" : 8.870364719583405\n },\n \"columns_name\" :\n [ \"Mode\", \"Region\", \"Source\", \"Share\", \"Data_Type\", \"Year\" ],\n \"columns_field_name\" :\n [ \"mode\", \"region\", \"source\", \"share\", \"data_type\", \"year\" ],\n \"columns_datatype\" :\n [ \"Text\", \"Text\", \"Text\", \"Number\", \"Text\", \"Number\" ],\n \"columns_description\" : [ \"\", \"\", \"\", \"\", \"\", \"\" ],\n \"columns_format\" : [ {}, {}, {}, {}, {}, { \"noCommas\" : \"true\" } ],\n \"download_count\" : 57,\n \"provenance\" : \"official\",\n \"lens_view_type\" : \"tabular\",\n \"lens_display_type\" : \"table\",\n \"locked\" : false,\n \"blob_mime_type\" : null,\n \"hide_from_data_json\" : false,\n \"publication_date\" : \"2020-04-09T17:07:03.000Z\"\n },\n \"classification\" :\n {\n \"categories\" : [ \"transportation\", \"demographics\" ],\n \"tags\" : [],\n \"domain_tags\" : [ \"vital signs\" ],\n \"domain_metadata\" : []\n },\n \"metadata\" : { \"domain\" : \"data.bayareametro.gov\" },\n \"permalink\" : \"https://data.bayareametro.gov/d/9mau-as85\",\n \"link\" : \"https://data.bayareametro.gov/dataset/Vital-Signs-Commute-Mode-Choice-by-Place-of-Reside/9mau-as85\",\n \"owner\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n },\n \"creator\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n }\n },\n {\n \"resource\" :\n {\n \"name\" : \"Employment First Annual OVR Outcomes Current Statewide Labor & Industry\",\n \"id\" : \"uimv-hpcj\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"The following are a selection of annual outcomes of services provided by the Pennsylvania's Department of Labor & Industry's Office of Vocational Rehabilitation. Outcomes include applicants and case outcomes including employment and wages.\\n\\nKey Footnotes:\\n1) Employed in Competitive Labor Market means employment at or above the minimum wage in settings where most employees do not have disabilities.\\n2) Estimated Taxes Paid are based on a standard deduction for the year, annual tax brackets and rates established by the IRS, and flat-rate FICA, state, and local taxes.\\n3) Estimated Total Government Savings are estimated federal, state, and local taxes paid plus annualized public support dollars at closure.\\n4) Average per Person Cost for a Competitive Employment Placement is the average individual \\\"life of case\\\" cost for all persons having a competitive employment outcome regardless of total number of years receiving services.\\n5) Average per Person Cost of Services is the average individual \\\"life of case\\\" cost for all persons having an employment outcome regardless of total number of years receiving services.\\n6) Source: U.S. Department of Labor, Bureau of Labor Statistics, May 2016 State Occupational Employment and Wage Estimates, Pennsylvania, https://www.bls.gov/oes/current/oes_pa.htm#00-0000.\",\n \"attribution\" : \"Department of Labor and Industry\",\n \"attribution_link\" : null,\n \"contact_email\" : null,\n \"type\" : \"dataset\",\n \"updatedAt\" : \"2023-02-08T19:15:06.000Z\",\n \"createdAt\" : \"2021-06-03T16:03:29.000Z\",\n \"metadata_updated_at\" : \"2023-02-08T19:15:06.000Z\",\n \"data_updated_at\" : \"2023-02-08T19:14:57.000Z\",\n \"page_views\" :\n {\n \"page_views_last_week\" : 7,\n \"page_views_last_month\" : 16,\n \"page_views_total\" : 870,\n \"page_views_last_week_log\" : 3.0,\n \"page_views_last_month_log\" : 4.08746284125034,\n \"page_views_total_log\" : 9.766528908598865\n },\n \"columns_name\" :\n [\n \"Percentage of Applicants Found Eligible\",\n \"New Applicants for Services\",\n \"Average Hourly Wage of Individuals Employed\",\n \"Projected Time (in Months) to Recover Investment\",\n \"Period\",\n \"Individuals Employed in the Competitive Labor Market (1)\",\n \"Cases Open 00-39 at end of Year\",\n \"Applicants Found Eligible\",\n \"Average Time in Months from Acceptance to Successful Closure\",\n \"Average per Person Cost of Services \",\n \"Average per Person Cost for a Competitive Employment Placement (4)\",\n \"Estimated Total Government Savings\",\n \"Estimated Federal, State, and Local Taxes Paid By These New Workers\",\n \"Percent of Individuals Employed in the Competitive Labor Market\",\n \"Individuals Placed into Employment\",\n \"Cases Closed During Year\",\n \"Individuals engaged with OVR during the Program Year\"\n ],\n \"columns_field_name\" :\n [\n \"percentage_of_applicants\",\n \"new_applicants_for_services\",\n \"average_hourly_wage_of\",\n \"projected_time_in_months\",\n \"period\",\n \"individuals_employed_in_the\",\n \"cases_open_00_39_at_end_of\",\n \"applicants_found_eligible\",\n \"average_time_in_months_from\",\n \"average_per_person_cost_of\",\n \"average_per_person_cost_for\",\n \"estimated_total_government\",\n \"estimated_federal_state_and\",\n \"percent_of_individuals\",\n \"individuals_placed_into\",\n \"cases_closed_during_year\",\n \"individuals_engaged_with\"\n ],\n \"columns_datatype\" :\n [\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Text\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\",\n \"Number\"\n ],\n \"columns_description\" :\n [\n \"Applicants Found Eligible divided by New Applicants\",\n \"Old Status Code 02 between 7/1 -6/30; distinct PIDS (Case History Report PY2019)\",\n \"PA average for all occupations (2019): $24.686 RSA standard: \\\"VR consumers served by general/combined agencies who achieved competitive outcomes are earning, on the average, at least 52 cents for every dollar earned hourly by all employed individuals in the state.\\\" OVR Program Year 2019 ratio: 57 cents for every dollar earned hourly by all employed individuals in the state (calculated as $14.11/$24.68), exceeding the RSA standard by 5 cents, or 9.94.%. OVR Program Year 2019 Average Hourly wages: From 'PY2019 26 Closures for Highlight' sheet - 1 Tab, column 'K' (Average of DE359 from Q4 PY2019 RSA911 for cases closed DE354 as \\\"6\\\" and DE353 within PY2019 was $14.30)\",\n \"Time to recover investment in months.\",\n \"Identifies the Federal Fiscal Year or State Program Year for the provided statistics.\",\n \"Employment in Competitive Placement means employment at or above minimum wage in settings where most employees do not have disabilities. (1)\",\n \"Ad hoc Case History Report *PY2019 include a period when the Order of Selection was Closed\",\n \"Old Status Code 10 between 7/1 -6/30; distinct PIDS (Case History Report PY2019)\",\n \"\",\n \"Average individual “life of case” cost for all persons having a competitive employment outcome regardless of total number of years receiving services.\",\n \"Average individual “life of case” cost for all persons having a competitive employment outcome regardless of total number of years receiving services.\",\n \"Estimated federal, state, and local taxes paid plus annualized public support dollars at closure.\",\n \"Based on a standard deduction for the year, annual tax brackets and rates established by the IRS, and flat-rate FICA, state, and local taxes.\",\n \"Individuals placed in Competitive Employment divided by the total placements in all employment types.\",\n \"Adhoc History Report (RSA-911 DE354, Type of Exit code 6=6932)\",\n \"RSA-911 closures; RSA-911 DE354, excludes -1 to -2 and 00 to 08 closures\",\n \"FFY 2015-16: 51,267 (RPT019, Open 00-39, 10/1-9/30) + 21,208 (RSA-113 Year-end Report, Line D8) PY 2016-17: 49,636 (RPT019, Open 00-39 as of 6/30/2017) + 24,958 (All closures during the Program Year) PY 2017-18: 50,394 cases open 00-39 as of 6/30/2018 (ad hoc) + 21,940 closures during the Program Year (RSA-911; excludes -1--2 and 00-08 closures) PY 2018-19: 47,425 cases open 00-39 as of 6/30/2019 (ad hoc) + 21,996 closures during the Program Year (RSA-911, validated in ad hoc, excludes -1 to -2 and 00 to 08 closures). PY 2019-20: 36,014 cases open 00-39 as of 6/30/2020 (ad hoc) + 18,535 closures during the Program Year (RSA-911 DE354, excludes -1 to -2 and 00 to 08 closures). *PY2019 include a period when the Order of Selection was Closed\"\n ],\n \"columns_format\" :\n [\n { \"precisionStyle\" : \"percentage\", \"percentScale\" : \"1\" },\n {},\n { \"precisionStyle\" : \"currency\", \"currencyStyle\" : \"USD\" },\n { \"precision\" : \"1\" },\n {},\n {},\n {},\n {},\n { \"precision\" : \"1\" },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n { \"precisionStyle\" : \"percentage\", \"percentScale\" : \"1\" },\n {},\n {},\n {}\n ],\n \"download_count\" : 100,\n \"provenance\" : \"official\",\n \"lens_view_type\" : \"tabular\",\n \"lens_display_type\" : \"table\",\n \"locked\" : false,\n \"blob_mime_type\" : null,\n \"hide_from_data_json\" : false,\n \"publication_date\" : \"2023-02-08T19:15:06.000Z\"\n },\n \"classification\" :\n {\n \"categories\" : [ \"economy\", \"social services\", \"finance\" ],\n \"tags\" : [],\n \"domain_category\" : \"Employment First\",\n \"domain_tags\" :\n [\n \"labor\",\n \"disability\",\n \"dli\",\n \"employment\",\n \"employment first\",\n \"labor and industry\",\n \"l&i\",\n \"outcomes\",\n \"ovr\",\n \"vocational\"\n ],\n \"domain_metadata\" :\n [\n {\n \"key\" : \"Data-Management_Business-Owner\",\n \"value\" : \"Department of Labor and Industry (DLI)\"\n },\n {\n \"key\" : \"Data-Management_Update-Frequency\",\n \"value\" : \"Annually\"\n }\n ]\n },\n \"metadata\" :\n {\n \"domain\" : \"data.pa.gov\",\n \"license\" : \"Public Domain U.S. Government\"\n },\n \"permalink\" : \"https://data.pa.gov/d/uimv-hpcj\",\n \"link\" : \"https://data.pa.gov/Employment-First/Employment-First-Annual-OVR-Outcomes-Current-State/uimv-hpcj\",\n \"owner\" :\n {\n \"id\" : \"s9q8-jynp\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"John Long\"\n },\n \"creator\" :\n {\n \"id\" : \"s9q8-jynp\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"John Long\"\n }\n },\n {\n \"resource\" :\n {\n \"name\" : \"Citizen Satisfaction Survey (2018-2021)\",\n \"id\" : \"btc8-9kef\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"These are the results of the 2018 to 2021 Citizen Satisfaction survey. To see the most recent reports, visit <a href=\\\"https://calgary.ca/citizensatisfaction\\\">Calgary.ca/citizensatisfaction</a>.\\n\\nFor detailed information on the variables in this dataset, <a href=\\\"https://data.calgary.ca/api/views/btc8-9kef/files/8edca0d5-637f-4333-aaf1-f00a61a1fc11?download=true&filename=Variable-metadata%202018-2021.xlsx\\\">see the variable metadata</a>.\\n\\nMethodology\\nThe Citizen Satisfaction survey is a telephone survey conducted with a randomly selected sample of Calgarians aged 18 years and older. The survey is conducted on an annual basis.\\nThe data are weighted to ensure the overall sample’s quadrant, ward, and age/gender composition reflects that of the actual Calgary population aged 18 or older according to Municipal and Federal Census data. Weights are included in the dataset.\\n\\n<a href=\\\"https://data.calgary.ca/api/views/btc8-9kef/files/8edca0d5-637f-4333-aaf1-f00a61a1fc11?download=true&filename=Variable-metadata%202018-2021.xlsx\\\">The variable metadata</a> contains detailed information on the variables.\",\n \"attribution\" : \"The City of Calgary\",\n \"attribution_link\" : \"https://calgary.ca/citizensatisfaction\",\n \"contact_email\" : null,\n \"type\" : \"dataset\",\n \"updatedAt\" : \"2023-02-01T15:39:18.000Z\",\n \"createdAt\" : \"2021-06-10T19:07:06.000Z\",\n \"metadata_updated_at\" : \"2023-02-01T15:39:18.000Z\",\n \"data_updated_at\" : \"2022-05-31T20:31:33.000Z\",\n \"page_views\" :\n {\n \"page_views_last_week\" : 14,\n \"page_views_last_month\" : 34,\n \"page_views_total\" : 2443,\n \"page_views_last_week_log\" : 3.9068905956085187,\n \"page_views_last_month_log\" : 5.129283016944966,\n \"page_views_total_log\" : 11.255028569818728\n },\n \"columns_name\" :\n [\n \"q9_2_11\",\n \"q8_24\",\n 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Corridor (Updated October 2018)\",\n \"id\" : \"f57x-8ifw\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"VITAL SIGNS INDICATOR\\nTime Spent in Congestion (T7)\\n\\nFULL MEASURE NAME\\nTime Spent in Congestion\\n\\nLAST UPDATED\\nOctober 2018\\n\\nDATA SOURCE\\nMTC/Iteris Congestion Analysis\\nNo link available\\n\\nCA Department of Finance Forms E-8 and E-5\\nhttp://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/\\nhttp://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/\\n\\nCA Employment Division Department: Labor Market Information\\nhttp://www.labormarketinfo.edd.ca.gov/\\n\\nCONTACT INFORMATION\\nvitalsigns.info@bayareametro.gov\\n\\nMETHODOLOGY NOTES (across all datasets for this indicator)\\nTime spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability.\\n\\nThis congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit.\\n\\nTo provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. \\n\\nMTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. \\n\\nData sources listed above were used to calculate per-capita and per-worker statistics. 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The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.\\n\\nDATA SOURCE\\nU.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census\\nhttp://www.bayareacensus.ca.gov/transportation/Means19802000.htm\\n\\nU.S. Census Bureau: American Community Survey\\nForm B08301 (2006-2018; place of residence)\\nwww.api.census.gov\\n\\nCONTACT INFORMATION\\nvitalsigns.info@bayareametro.gov\\n\\nMETHODOLOGY NOTES (across all datasets for this indicator)\\nFor the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. \\\"Other\\\" includes bicycle, motorcycle, taxi, and other modes of transportation.\\n\\nFor the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode shares are population-weighted averages of the nine counties’ modal shares. \\\"Auto\\\" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. “Transit” includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). \\\"Other\\\" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.\\n\\nThe metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.\",\n \"attribution\" : \"U.S. Census Bureau\",\n \"attribution_link\" : \"https://data.census.gov\",\n \"contact_email\" : null,\n \"type\" : \"dataset\",\n \"updatedAt\" : \"2020-05-20T21:50:47.000Z\",\n \"createdAt\" : \"2020-04-09T17:01:10.000Z\",\n \"metadata_updated_at\" : \"2020-05-20T21:50:47.000Z\",\n \"data_updated_at\" : \"2020-04-15T20:55:37.000Z\",\n \"page_views\" :\n {\n \"page_views_last_week\" : 4,\n \"page_views_last_month\" : 9,\n \"page_views_total\" : 467,\n \"page_views_last_week_log\" : 2.321928094887362,\n \"page_views_last_month_log\" : 3.3219280948873626,\n \"page_views_total_log\" : 8.870364719583405\n },\n \"columns_name\" :\n [ \"Mode\", \"Region\", \"Source\", \"Share\", \"Data_Type\", \"Year\" ],\n \"columns_field_name\" :\n [ \"mode\", \"region\", \"source\", \"share\", \"data_type\", \"year\" ],\n \"columns_datatype\" :\n [ \"Text\", \"Text\", \"Text\", \"Number\", \"Text\", \"Number\" ],\n \"columns_description\" : [ \"\", \"\", \"\", \"\", \"\", \"\" ],\n \"columns_format\" : [ {}, {}, {}, {}, {}, { \"noCommas\" : \"true\" } ],\n \"download_count\" : 57,\n \"provenance\" : \"official\",\n \"lens_view_type\" : \"tabular\",\n \"lens_display_type\" : \"table\",\n \"locked\" : false,\n \"blob_mime_type\" : null,\n \"hide_from_data_json\" : false,\n \"publication_date\" : \"2020-04-09T17:07:03.000Z\"\n },\n \"classification\" :\n {\n \"categories\" : [ \"transportation\", \"demographics\" ],\n \"tags\" : [],\n \"domain_tags\" : [ \"vital signs\" ],\n \"domain_metadata\" : []\n },\n \"metadata\" : { \"domain\" : \"data.bayareametro.gov\" },\n \"permalink\" : \"https://data.bayareametro.gov/d/9mau-as85\",\n \"link\" : \"https://data.bayareametro.gov/dataset/Vital-Signs-Commute-Mode-Choice-by-Place-of-Reside/9mau-as85\",\n \"owner\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n },\n \"creator\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n }\n },\n {\n \"resource\" :\n {\n \"name\" : \"Employment First Annual OVR Outcomes Current Statewide Labor & Industry\",\n \"id\" : \"uimv-hpcj\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"The following are a selection of annual outcomes of services provided by the Pennsylvania's Department of Labor & Industry's Office of Vocational Rehabilitation. 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OVR Program Year 2019 Average Hourly wages: From 'PY2019 26 Closures for Highlight' sheet - 1 Tab, column 'K' (Average of DE359 from Q4 PY2019 RSA911 for cases closed DE354 as \\\"6\\\" and DE353 within PY2019 was $14.30)\",\n \"Time to recover investment in months.\",\n \"Identifies the Federal Fiscal Year or State Program Year for the provided statistics.\",\n \"Employment in Competitive Placement means employment at or above minimum wage in settings where most employees do not have disabilities. 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PY 2019-20: 36,014 cases open 00-39 as of 6/30/2020 (ad hoc) + 18,535 closures during the Program Year (RSA-911 DE354, excludes -1 to -2 and 00 to 08 closures). *PY2019 include a period when the Order of Selection was Closed\"\n ],\n \"columns_format\" :\n [\n { \"precisionStyle\" : \"percentage\", \"percentScale\" : \"1\" },\n {},\n { \"precisionStyle\" : \"currency\", \"currencyStyle\" : \"USD\" },\n { \"precision\" : \"1\" },\n {},\n {},\n {},\n {},\n { \"precision\" : \"1\" },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n {\n \"precisionStyle\" : \"currency\",\n \"currencyStyle\" : \"USD\",\n \"precision\" : \"0\"\n },\n { \"precisionStyle\" : \"percentage\", \"percentScale\" : \"1\" },\n {},\n {},\n {}\n ],\n \"download_count\" : 100,\n \"provenance\" : \"official\",\n \"lens_view_type\" : \"tabular\",\n \"lens_display_type\" : \"table\",\n \"locked\" : false,\n \"blob_mime_type\" : null,\n \"hide_from_data_json\" : false,\n \"publication_date\" : \"2023-02-08T19:15:06.000Z\"\n },\n \"classification\" :\n {\n \"categories\" : [ \"economy\", \"social services\", \"finance\" ],\n \"tags\" : [],\n \"domain_category\" : \"Employment First\",\n \"domain_tags\" :\n [\n \"labor\",\n \"disability\",\n \"dli\",\n \"employment\",\n \"employment first\",\n \"labor and industry\",\n \"l&i\",\n \"outcomes\",\n \"ovr\",\n \"vocational\"\n ],\n \"domain_metadata\" :\n [\n {\n \"key\" : \"Data-Management_Business-Owner\",\n \"value\" : \"Department of Labor and Industry (DLI)\"\n },\n {\n \"key\" : \"Data-Management_Update-Frequency\",\n \"value\" : \"Annually\"\n }\n ]\n },\n \"metadata\" :\n {\n \"domain\" : \"data.pa.gov\",\n \"license\" : \"Public Domain U.S. Government\"\n },\n \"permalink\" : \"https://data.pa.gov/d/uimv-hpcj\",\n \"link\" : \"https://data.pa.gov/Employment-First/Employment-First-Annual-OVR-Outcomes-Current-State/uimv-hpcj\",\n \"owner\" :\n {\n \"id\" : \"s9q8-jynp\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"John Long\"\n },\n \"creator\" :\n {\n \"id\" : \"s9q8-jynp\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"John Long\"\n }\n },\n {\n \"resource\" :\n {\n \"name\" : \"Citizen Satisfaction Survey (2018-2021)\",\n \"id\" : \"btc8-9kef\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"These are the results of the 2018 to 2021 Citizen Satisfaction survey. To see the most recent reports, visit <a href=\\\"https://calgary.ca/citizensatisfaction\\\">Calgary.ca/citizensatisfaction</a>.\\n\\nFor detailed information on the variables in this dataset, <a href=\\\"https://data.calgary.ca/api/views/btc8-9kef/files/8edca0d5-637f-4333-aaf1-f00a61a1fc11?download=true&filename=Variable-metadata%202018-2021.xlsx\\\">see the variable metadata</a>.\\n\\nMethodology\\nThe Citizen Satisfaction survey is a telephone survey conducted with a randomly selected sample of Calgarians aged 18 years and older. The survey is conducted on an annual basis.\\nThe data are weighted to ensure the overall sample’s quadrant, ward, and age/gender composition reflects that of the actual Calgary population aged 18 or older according to Municipal and Federal Census data. 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As such certain industries and/or geographic areas have been suppressed \"\n },\n {\n \"key\" : \"Disclaimers_Limitations\",\n \"value\" : \"Certain data are suppressed to maintain confidentiality of employers. This dataset is only computed once all four quarters are released. Government ownership firms are allocated to either Federal, State, or Local. 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For the non-New\\nYork City counties, the code refers to the\\nalphabetical order of the district name (e.g.,\\nAlbany=01, Yates = 57). 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Corridor (Updated October 2018)\",\n \"id\" : \"f57x-8ifw\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"VITAL SIGNS INDICATOR\\nTime Spent in Congestion (T7)\\n\\nFULL MEASURE NAME\\nTime Spent in Congestion\\n\\nLAST UPDATED\\nOctober 2018\\n\\nDATA SOURCE\\nMTC/Iteris Congestion Analysis\\nNo link available\\n\\nCA Department of Finance Forms E-8 and E-5\\nhttp://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/\\nhttp://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/\\n\\nCA Employment Division Department: Labor Market Information\\nhttp://www.labormarketinfo.edd.ca.gov/\\n\\nCONTACT INFORMATION\\nvitalsigns.info@bayareametro.gov\\n\\nMETHODOLOGY NOTES (across all datasets for this indicator)\\nTime spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability.\\n\\nThis congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit.\\n\\nTo provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. \\n\\nMTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. \\n\\nData sources listed above were used to calculate per-capita and per-worker statistics. 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\"metadata\" : { \"domain\" : \"data.bayareametro.gov\" },\n \"permalink\" : \"https://data.bayareametro.gov/d/f57x-8ifw\",\n \"link\" : \"https://data.bayareametro.gov/dataset/Vital-Signs-Time-in-Congestion-Corridor-Updated-Oc/f57x-8ifw\",\n \"owner\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n },\n \"creator\" :\n {\n \"id\" : \"wg3i-tdm6\",\n \"user_type\" : \"interactive\",\n \"display_name\" : \"Raleigh McCoy\"\n }\n },\n {\n \"resource\" :\n {\n \"name\" : \"ED Private Establishments & Wages QCEW\",\n \"id\" : \"26kz-atns\",\n \"resource_name\" : null,\n \"parent_fxf\" : [],\n \"description\" : \"Numbers of private-sector establishments in Pierce County, by quarter, reporting information about employment and wages on the Quarterly Census of Employment and Wages.\",\n \"attribution\" : \"Bureau of Labor Statistics\",\n \"attribution_link\" : \"https://www.bls.gov/cew/\",\n \"contact_email\" : null,\n \"type\" : 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Data is a weekly average from January 2017 through current. \\n\\nAverage daily retail gasoline prices are collected from the American Automobile Association (AAA) Daily Fuel Gauge Report. The AAA Daily Fuel Gauge Report prices are averaged to produce a weekly average retail price for New York State and each metropolitan region.\\n\\nThe New York State metropolitan regions in the dataset are Albany (Albany-Schenectady-Troy), Batavia, Binghamton, Buffalo (Buffalo-Niagara Falls), Dutchess (Dutchess-Putnam), Elmira, Glens Falls, Ithaca, Kingston, Nassau (Nassau-Suffolk), New York City, Rochester, Syracuse, Utica (Utica-Rome), Watertown (Watertown-Fort Drum), and White Plains.\\n\\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. 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