These are the data for displayed in the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2024, but display data from 2022 and 2023. Most data are from the 2022 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for: Total Population - City of Austin Planning Department (2023) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (2022) Occupied Housing Units - City of Austin Planning Department (2023) Median Home Closing Price - Austin Board of Realtors (2023) Average Monthly Rent - Austin Investor Interests (Q4 2023) Income Restricted Units - City of Austin Affordable Housing Inventory Housing Units-City of Austin Planning Department (2023) Population Density - Esri Updated Demographics Daytime Population Density - Esri Updated Demographics Selected Land Use Percentages - City of Austin Land Use Inventory Transit Stops - Capital Metro (2023) City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates. More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics. These profiles are updated annually. City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

These are the data for the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2025, but display data from 2023 and 2024. Most data are from the 2023 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for: Total Population - City of Austin Planning Department (2023) (City and Council Districts only) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (5 year 2016-2020) Occupied Housing Units - City of Austin Planning Department (2023) (City and Council Districts only) Median Home Closing Price - Austin Board of Realtors (2024) Average Monthly Rent - ApartmentTrends.com by Austin Investor Interests (Q4 2024) Income Restricted Units - City of Austin Affordable Housing Inventory (March 2025) Housing Units - City of Austin Planning Department (2023)(City only) Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Daytime Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Population Density - Calculation derived from 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Daytime Population Density - 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Selected Land Use Percentages - City of Austin Land Use Inventory (2024) Transit Stops - Capital Metro (January 2025) City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates. Some datapoints may not be available for all geographies. More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics. These profiles are updated annually. City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics. A resident in a complete community is someone residing in an area that is within a 20 minute walk to multiple essential destinations. Calculation method: This study measured the distance and time it takes for a pedestrian to reach five essential destination, or "indicators," from any point across the city using the existing network of sidewalks and crossings within a 20-minute walk time. Using GIS software, this evaluation resulted in a rasterized overlay of geographic outlines of “walksheds” surrounding each indicator destination. Residential estimates were found using an internal database of residential housing units and applied density assumptions and should not be compared to other demographic datasets. Data was sourced from City of Austin, CapMetro, and Austin ISD. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/rw4g-mrjp

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

PARD’s Long Range Plan for Land, Facilities and Programs, Our Parks, Our Future (adopted November 2019) compared Austin’s park system to five peer cities: Atlanta, GA, Dallas, TX, Portland, OR, San Antonio, TX, and San Diego, CA. The peer cities were selected based on characteristics such as population, size, density, and governance type. Portland and San Diego were selected as aspirational cities known for their park systems.

Note that the table below presents each scoring area’s 1 to 100 index, where 100 is the highest possible score.

Intraspecific competition influences population and community dynamics and occurs via two mechanisms. Exploitative competition is an indirect effect that occurs through use of a shared resource and depends on resource availability. Interference competition occurs by obstructing access to a resource and may not depend on resource availability. Our study tested whether the strength of interference competition changes with protozoa population density. We grew experimental microcosms of protozoa and bacteria under different combinations of protozoan density and basal resource availability. We then solved a dynamic predator–prey model for parameters of the functional response using population growth rates measured in our experiment. As population density increased, competition shifted from exploitation to interference, and competition was less dependent on resource levels. Surprisingly, the effect of resources was weakest when competition was the most intense. We found that at low population densities, competition was largely exploitative and resource availability had a large effect on population growth rates, but the effect of resources was much weaker at high densities. This shift in competitive mechanism could have implications for interspecific competition, trophic interactions, community diversity, and natural selection. We also tested whether this shift in the mechanism of competition with protozoa density affected the structure of the bacterial prey community. We found that both resources and protozoa density affected the structure of the bacterial prey community, suggesting that competitive mechanism may also affect trophic interactions.

Dispersal moves individuals from patches where their immediate ancestors were successful to sites where their genotypes are untested. As a result, dispersal generally reduces fitness, a phenomenon known as “migration load.” The strength of migration load depends on the pattern of dispersal and can be dramatically lessened or reversed when individuals move preferentially toward patches conferring higher fitness. Evolutionary ecologists have long modeled nonrandom dispersal, focusing primarily on its effects on population density over space, the maintenance of genetic variation, and reproductive isolation. Here, we build upon previous work by calculating how the extent of local adaptation and the migration load are affected when individuals differ in their dispersal rate in a genotype-dependent manner that alters their match to their environment. Examining a one-locus, two-patch model, we show that local adaptation occurs through a combination of natural selection and adaptive dispersal. For a substantial portion of parameter space, adaptive dispersal can be the predominant force generating local adaptation. Furthermore, genetic load may be largely averted with adaptive dispersal whenever individuals move before selective deaths occur. Thus, to understand the mechanisms driving local adaptation, biologists must account for the extent and nature of nonrandom, genotype-dependent dispersal, and the potential for adaptation via spatial sorting of genotypes.