Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Austin County, TX (DISCONTINUED) (NETMIGNACS048015) from 2009 to 2020 about Austin County, TX; migration; Houston; flow; Net; TX; 5-year; and population.

DATASET DESCRIPTION: This dataset includes documented incidents in which APD inquired into a subject’s immigration status, in accordance with City of Austin Resolution 20180614-074. GENERAL ORDERS RELATING TO THIS DATA: Officers who have lawfully detained a person to conduct a criminal investigation into an alleged criminal offense, or who have arrested a person for a criminal offense, may make an inquiry into the person’s immigration status. Before officers inquire into immigration status, they must instruct the detainee or arrestee that the detainee or arrestee is not compelled to respond to the inquiry and that the detainee or arrestee will not be subjected to additional law enforcement action because of their refusal to respond. AUSTIN POLICE DATA DISCLAIMER: 1. The data provided is for informational use only and may differ from official Austin Police crime data. APD’s databases are continuously updated, and changes can be made due to a variety of investigative factors including but not limited to offense reclassification and dates. Reports run at different times may produce different results. Care should be taken when comparing against other reports as different data collection methods and different systems of record may have been used. APD does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. City of Austin Open Data Terms of Use - https://data.austintexas.gov/stories/s/ranj-cccq

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Travis County, TX (DISCONTINUED) (NETMIGNACS048453) from 2009 to 2020 about Travis County, TX; migration; Austin; flow; Net; TX; 5-year; and population.

The City of Austin’s “DTI 2060 Population and Employment Forecast” is a long-range, small-area population and employment forecast produced by the Demographics and Data Division in the Planning Department in conjunction with representatives from multiple City departments making up the DTI Work Group. DTI stands for Delphi, Trends, and Imagine Austin, and the "DTI 2060 Population and Employment Forecast” is an update to the "Population Projections 2040". The DTI work group produced population and employment forecasts within each polygon in the study area for the year 2025 and the decades from 2030 to 2060, using the year 2020 as the baseline and half of 2010’s migration trends. Potential population and employment growth were forecast within Imagine Austin activity centers and along mixed-use corridors using City staff knowledge of the trends within current development patterns and practices. The DTI 2060 forecast incorporates urban-centric future growth and development and accounts for widely-dispersed, low-density suburban development.

Net County-to-County Migration Flow (5-year estimate) for Austin County, TX was -495.00000 Persons in January of 2020, according to the United States Federal Reserve. Historically, Net County-to-County Migration Flow (5-year estimate) for Austin County, TX reached a record high of 487.00000 in January of 2015 and a record low of -599.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Net County-to-County Migration Flow (5-year estimate) for Austin County, TX - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Hays County, TX (DISCONTINUED) (NETMIGNACS048209) from 2009 to 2020 about Hays County, TX; migration; Austin; flow; Net; TX; 5-year; and population.

About this dataset

Context

The dataset tabulates the Austin population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Austin. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 672,618 (71.20% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Austin population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Austin is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Austin is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Austin Population by Age. You can refer the same here

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Williamson County, TX (DISCONTINUED) (NETMIGNACS048491) from 2009 to 2020 about Williamson County, TX; migration; Austin; flow; Net; TX; 5-year; and population.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Bastrop County, TX (DISCONTINUED) (NETMIGNACS048021) from 2009 to 2020 about Bastrop County, TX; migration; Austin; flow; Net; TX; 5-year; and population.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Caldwell County, TX (DISCONTINUED) (NETMIGNACS048055) from 2009 to 2020 about Caldwell County, TX; migration; Austin; flow; Net; TX; 5-year; and population.

Climate change poses a significant threat to the resilience and sustainability of forest ecosystems. This study examines the performance of white oak (Quercus alba, L.) across a range of provenances in a common garden planting, focusing on the species’ response to climatic variables and the potential role of assisted migration in forest management. We evaluated the survival and growth rates of white oak provenances originating from various points along a latitudinal gradient over a period of 40 years. These provenances were planted in a common garden situated near the midpoint of this latitudinal gradient, where we also monitored their phenological traits, such as budburst and leaf senescence. The results revealed substantial variation in phenological responses and growth patterns among the provenances, with southern provenances demonstrating faster growth and later senescence relative to local sources, with limited impact on survival. In contrast, the northern provenances demonstrated ..., Data were collected as described in Thomas AM, Coggeshall MV, O’Connor PA, Nelson CD. Climate Adaptation in White Oak (Quercus alba, L.): A Forty-Year Study of Growth and Phenology. Forests. 2024; 15(3):520. https://doi.org/10.3390/f15030520. Briefly, tree height, DBH, and phenological measurements were collected at the Starve-Hollow State Recreation Area by study authors throughout the 40-year study. Increment cores were collected in 2023 and mounted for precision measurement using a Velmex TA measurement system. Contact Austin M. Thomas at austin.thomas@uky.edu or austin.thomas2@usda.gov for additional information., , # Postage stamp common garden 40yr provenance

Access this dataset on Dryad

Complete data related to the 40-year-old (as of 2023) white oak (Quercus alba) "Postage Stamp" common garden planting located at Starve Hollow State Recreation area near Vallonia, Indiana. Contact Austin M. Thomas at austin.thomas@uky.edu or austin.thomas2@usda.gov for additional information.

These data include tree height and DBH measurements, as well as phenological and dendrochronological measurements.

Description of the data and file structure

Sections:

Section 1, Position and allometric data (col A-AF) Columns A through AF are position data and alometric measurements. Measurement and unit descriptions are in Row 1.

Location ID: Location ID in 'Row-Column' format. tree ID: Unique tree identifier. Col: Column number(excluding border trees). Column number increases from West to East. R...

About this dataset

Context

The dataset tabulates the Austin population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Austin. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 2,414 (61.57% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Austin population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Austin is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Austin is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Austin Population by Age. You can refer the same here

Roughly three billion people worldwide live along large rivers and rely upon them for food, water, transport, and energy. To ensure the safety and sustainability of these riverside communities, it is important that we understand how rivers migrate over time. Satellite missions like NASA Landsat have captured millions of images of migrating rivers worldwide for more than thirty years—more images than can be feasibly mapped manually. These data and codes accompany the manuscript "Remote Sensing of Riverbank Migration using Particle Image Velocimetry" by Austin J. Chadwick, Evan Greenberg, and Vamsi Ganti. In this manuscript, we build on previous work and present a method to automatically map riverbank migration from satellite images using a technique called particle image velocimetry (PIV). We apply PIV to Landsat-image time series for 21 example rivers and show PIV results are efficient, reproducible, and accurate compared with existing automatic techniques. Importantly, unlike existing ...

Roughly three billion people worldwide live along large rivers and rely upon them for food, water, transport, and energy. To ensure the safety and sustainability of these riverside communities, it is important that we understand how rivers migrate over time. Satellite missions like NASA Landsat have captured millions of images of migrating rivers worldwide for more than thirty years—more images than can be feasibly mapped manually.These data and codes accompany the manuscript "Remote Sensing of Riverbank Migration using Particle Image Velocimetry" by Austin J. Chadwick, Evan Greenberg, and Vamsi Ganti. In this manuscript, we build on previous work and present a method to automatically map riverbank migration from satellite images using a technique called particle image velocimetry (PIV). We apply PIV to Landsat-image time series for 21 example rivers and show PIV results are efficient, reproducible, and accurate compared with existing automatic techniques. Importantly, unlike existing t...

Why rivers confine flow to a single channel (single-thread planform) or divide flow into multiple sub-channels (multi-thread planform) forms a longstanding fundamental question in river science, which to date remains poorly understood. In the associated manuscript, we probe planform origins using a novel dataset of 11+ million riverbank migration vectors mapped from 36 years of global satellite imagery along 84 river systems. Results show single-thread rivers originate from a balance between bank erosion and opposing-bar deposition, which maintains an equilibrium width as channels migrate. In contrast, multi-thread rivers originate from imbalance: bank erosion outpaces opposing-bar deposition, causing sub-channels to repeatedly widen and split. This width instability challenges equilibrium paradigms in river science, endangers riverside communities, and lowers the potential costs of nature-based river restoration projects along multi-thread rivers. Here, we provide the data and codes th..., , , # Data and code for: River planforms originate from (im)balance between bank erosion and bar accretion

author: Austin Chadwick contact: achadwick@ldeo.columbia.edu, austin.chadwick23@gmail.com

These materials are organized into two folders, Codes and Data. The Data folder contains spreadsheets, GIS geopackages, remote sensing images, and MATLAB data files for the associated manuscript "River planforms originate from (im)balance between bank erosion and bar accretion". The Codes folder provides MATLAB scripts and functions to generate the analysis and figures of the main manuscript. The last folder, temp, is empty; it acts as a temporary destination for output files that can be generated by the codes.

Description of the data and file structure

All data are found in the folder "Data", which can be accessted by opening the zip file "Data.zip". The following is a breakdown of each file and subfolder in t...,

This article demonstrates how the party identification of various demographic groups in California and Texas changed in response to the gubernatorial campaigns of Pete Wilson and George W. Bush. Using aggregated time series of Field Poll, Texas Poll, and Gallup data, difference-in-differences results show that Wilson's embrace of Proposition 187 was followed by significant Hispanic movement towards the Democratic Party in California. Time series analysis substantiates that this action led to a long-term 7.1 percentage point Democratic shift among California's Hispanics. This suggests that state-level actors can influence partisan coalitions in their state, beyond what would be expected from national-level factors. Data Description Public opinion data on United States and California macropartisanship, or mass party identification, from 1969-2010. Based on Gallup and Field Poll results, respectively. Also includes US-level measures of consumer sentiment, presidential approval, presidential party, and indicator for presidential administration. Additional data are from the Texas Poll from 1990-1998 to capture macropartisanship in Texas.