Graph and download economic data for Unemployment Rate in Austin-Round Rock, TX (MSA) (AUST448URN) from Jan 1990 to May 2025 about Austin, TX, unemployment, rate, and USA.

Graph and download economic data for Unemployment Rate in Austin County, TX (TXAUST5URN) from Jan 1990 to Jun 2025 about Austin County, TX; Houston; TX; unemployment; rate; and USA.

Unemployment Rate in Austin County, TX was 3.40% in April of 2025, according to the United States Federal Reserve. Historically, Unemployment Rate in Austin County, TX reached a record high of 9.40 in July of 2011 and a record low of 2.40 in April of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Austin County, TX - last updated from the United States Federal Reserve on June of 2025.

This dataset contains information about the unemployment rate in Austin (SD23 measure EOA.A.1). Texas Workforce Comission provides Texas Labor Market Information for Austin, the Austin Round-Rock MSA, Texas, and the United States. This dataset includes the average number of people in the civilian labor force, the employment count, the unemployment count, and the unemployment rate for Austin, the Austin Round-Rock MSA, Texas, and the United States. The unemployment rate can be useful in understanding economic and workforce trends in Austin over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percentage-Unemployment-Rate/ehhu-nafn/

Graph and download economic data for Unemployment Rate in Austin County, TX (LAUCN480150000000003A) from 1990 to 2024 about Austin County, TX; Houston; TX; unemployment; rate; and USA.

Austin-Round Rock-Georgetown, TX - Unemployment Rate in Austin-Round Rock, TX (MSA) was 3.40% in May of 2025, according to the United States Federal Reserve. Historically, Austin-Round Rock-Georgetown, TX - Unemployment Rate in Austin-Round Rock, TX (MSA) reached a record high of 11.80 in April of 2020 and a record low of 2.20 in August of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for Austin-Round Rock-Georgetown, TX - Unemployment Rate in Austin-Round Rock, TX (MSA) - last updated from the United States Federal Reserve on July of 2025.

The Texas Workforce Commission provides Texas Labor Market Information with counts for the civilian labor force, employment, unemployment, and unemployment rate estimates by place of residence. According to the U.S. Bureau of Labor Statistics, the definition of unemployed is to be "jobless, actively seeking work, and available to take a job." The unemployment rate is an important indicator of economic and workforce health in Austin over time. Unemployment Rate for the City of Austin = Number of Unemployed / Civilian Labor Force

Graph and download economic data for Unemployment Rate in Travis County, TX (TXTRAV3URN) from Jan 1990 to Jun 2025 about Travis County, TX; Austin; TX; unemployment; rate; and USA.

Analysis of ‘Strategic Measure_Percentage Unemployment Rate’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fb383985-5de9-4f55-ba17-581333f28ba9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains information about the unemployment rate in Austin (SD23 measure EOA.A.1). Texas Workforce Comission provides Texas Labor Market Information for Austin, the Austin Round-Rock MSA, Texas, and the United States.

This dataset includes the average number of people in the civilian labor force, the employment count, the unemployment count, and the unemployment rate for Austin, the Austin Round-Rock MSA, Texas, and the United States. The unemployment rate can be useful in understanding economic and workforce trends in Austin over time.

View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percentage-Unemployment-Rate/ehhu-nafn/

--- Original source retains full ownership of the source dataset ---

Unemployment Rate in Austin County, TX was 3.70% in January of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in Austin County, TX reached a record high of 8.10 in January of 2010 and a record low of 3.30 in January of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Austin County, TX - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Unemployment Rate in Williamson County, TX (TXWILL5URN) from Jan 1990 to May 2025 about Williamson County, TX; Austin; TX; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Hays County, TX (TXHAYS9URN) from Jan 1990 to Apr 2025 about Hays County, TX; Austin; TX; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Williamson County, TX (LAUCN484910000000003A) from 1990 to 2024 about Williamson County, TX; Austin; TX; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Caldwell County, TX (TXCALD5URN) from Jan 1990 to Apr 2025 about Caldwell County, TX; Austin; TX; unemployment; rate; and USA.

Date of Publication: 07/21/2021Name of Person Responsible: Alan HalterDate to be removed/updated: Ongoing updates. Last updated on 10/61/2021.This map includes the variables used to calculate Tree Equity Scores for Austin, Texas. For more information, contact the original data author, American Forests. Layer colors are HEX F99D3E (orange) to 6CC396 (green).A Tree Equity Score is a metric that helps cities assess how well they are delivering equitable tree canopy cover to all residents. The score combines measures of tree canopy cover need and priority for trees in urban neighborhoods (defined as Census Block Groups). It is derived from tree canopy cover, climate, demographic and socioeconomic data. Geographies represent selected Census blockgroups for Caldwell, Hays, Travis, and Williamson counties. They cover the Census "urbanized area" for Austin and might not represent the full City of Austin jurisdiction.The score is calculated at the neighborhood (block group) level.Methodology (For more information about methodology, visit https://treeequityscore.org/methodology/ )Step 1: A Neighborhood GoalDensity Adjusted Canopy TargetThe canopy target – which is meant to be equitable, aspirational and achievable – requires the following data:Tree canopy cover. High resolution tree canopy where available, the National Land Cover Database where it is not.Census American Community Survey (ACS) 2018 5-year Block Group population estimatesCensus ACS 2018 5-year city and block group Median Income estimatesTo identify a baseline canopy target, we use generalized natural biome baseline targets selected in conjunction with the USDA Forest Service. We select the baseline target based on the location of the municipality.Forest: 40%Grassland: 20%Desert: 15%This target is then adjusted based on population density to estimate a neighborhood goal. Based on research completed by The Nature Conservancy, adjustments are made using the following table:Adjusting for population density makes for more achievable targets, while recognizing differences in plantable areas suitable for tree canopy. Note: Neighborhood goals are capped at 150% of the natural biome baseline target.The formula for each neighborhood goal, GOAL, is as follows:GOAL = Baseline target * Density adjustment factorStep 2: The Canopy GapThe neighborhood canopy gap, GAP, is calculated by subtracting the existing neighborhood canopy from the density adjusted target, that is: GAP = GOAL – EC, where EC is % existing canopy for that neighborhoodThe canopy Gap is then normalized to a score from 0-100.GAPScore = 100 * GAP / GAPmax , where:GAPmax is the maximum GAP value citywide for that indicator; andNotes: If the GAP is negative (i.e. Existing canopy is greater than the neighborhood goal), it is adjusted to 0 before normalizing to create the gap score. Also, if Gapmax = 0, then GapScore is set to 0 as well.Step 3: The Priority IndexThe Priority Index is developed to help prioritize the need for planting to achieve Tree Equity. The priority index includes the following equally-weighted characteristics:Income: Percentage of population below 200% of povertyEmployment: Unemployment rateRace: Percentage of people who are not white non-HispanicAge: Ratio of seniors and children to working-age adultsClimate: Urban Heat Island severityHealth: Prevalence of poor mental, physical, respiratory, and cardiac health (composite index)These measures are normalized and combined to create a simple priority index from 0 to 1, where 1 indicates a greater amount of inequity. The indices, N, are calculated as follows:Ni = (xi - xi,min ) / (xi,max - xi,min) , where, for each indicator, Ni,xi is the value for that neighborhood for that indicator, i;xi,max is the maximum value citywide for that indicator, i; andxi,min is the minimum value citywide for that indicator, i.The Priority index, E, is then calculated as follows: E = (N1 + N2 + N3 + N4 + N5 + N6) / 6 , where Ni refers to each indicator value (income, employment, race, age, or climate)Step 4: Tree Equity ScoreTree Equity Score, TES, is calculated by multiplying the Baseline Gap Score by the Priority Index, simply:TES = 100 (1 - GAPScore E)A lower Tree Equity Score indicates a greater priority for closing the tree canopy gap.Tree equity scores of 100 indicate tree equity has been achieved.Data Dictionarygeoid: the blockgroup idtotal_pop: the total population of the block groupstate: the state the blockgroup is incounty: the county the blockgroup is inpctpov: the percent of people in poverty inside the blockgrouppctpoc: the percent of people of color inside the block groupunemplrate: the unemployment rate inside of the block groupmedhhinc: the median household income of the block groupdep_ratio: the dependency ratio (childrens + seniors / 18-64 adults)child_perc: the percent of children inside of the blockgroupseniorperc: the percent of seniors inside of the blockgrouparea: the area of the blockgroup in square kilometerssource: the source of the tree canopy of the block groupavg_temp: the average temperature of the blockgroup on a hot summer's dayua_name: the urbanized area the block group is located insideincorpname: the incorporated place the block group is located insidecongressio: the congressional district of the block groupbgpopdense: the density of the blockgroup (total population over area)popadjust: the population adjustment factor (based on the population density)biome: the biome of the blockgroupbaselinecanopy: baseline tree canopy target generalized to natural biome (percent)treecanopy: the tree canopy percentage of the blockgroup (set to negative 1 if the source is 'ED')tc_gap: the tree canopy gap of the block group (goal minus canopy)tc_goal: the tree canopy goal of the block group (set to negative 1 if the source is 'ED')phys_hlth: the self reported physical health challenges of the people in the block group (a percentage)ment_hlth: the self reported mental health challenges of people in the block group (a percentage)asthma: the self reported asthma challenges of people in the block group (a percentage)core_m: the self reported male coronary heart challenges of people in the block group (a percentage)core_w: the self reported female coronary heart challenges of people in the block group (a percentage)core_norm: the normalized total coronary challenges of people in the block grouphealthnorm: the normalized health index of the block grouppriority: the priority index of the block grouptes: the tree equity score of the block grouptesctyscor: the tree equity score of the incorporated place/municipality of the block group

Graph and download economic data for Unemployment Rate in Bastrop County, TX (LAUCN480210000000003A) from 1990 to 2024 about Bastrop County, TX; Austin; TX; unemployment; rate; and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ATX Index: The Pulse of Austin's Economy?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Unemployment Rate in Bastrop County, TX (TXBAST1URN) from Jan 1990 to May 2025 about Bastrop County, TX; Austin; TX; unemployment; rate; and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ATX Index: The Future of Austin's Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

AUST Austin Gold Corp. Common Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data