In 2023, the population of the Phoenix-Mesa-Chandler metropolitan area in the United States was about 5.1 million people. This is a slight increase from the previous year, when the population was about 5.02 million people.

Chart and table of population level and growth rate for the Phoenix metro area from 1950 to 2025.

In 2022, the real gross domestic product (GDP) of the Phoenix metro area amounted to ****** billion U.S. dollars. This is a large increase from the GDP in 2001 which came to ****** billion U.S. dollars.

The Brookings Institute study concluded that: Steep employment losses following the Great Recession stalled the steady decentralization of jobs that characterized the early to mid-2000s. However, by 2010 nearly twice the share of jobs was located at least 10 miles away from downtown (43%) as within 3 miles of downtown (23%).Job losses in industries hit hardest by the downturn, including construction and manufacturing, helped check employment decentralization in the late 2000s. In all but nine of the 100 largest metro areas, the share of jobs located within three miles of downtown declined during the 2000s.Metro areas showing the greatest increase in jobs in the 10-35 miles radius from downtown include:Phoenix-Mesa-Glendale, AZ, San Antonio-New Braunfels, TX, Austin-Round Rock-San Marcos, TX, Dallas-Fort Worth-Arlington, TX, and Houston-Sugar Land-Baytown, TX.Metro areas showing the greatest loss of jobs within the 3 mile radius of downtown include:North Port-Bradenton-Sarasota, FL, Boise City-Nampa, ID, Jackson, MS, McAllen-Edinburg-Mission, TX, and Cape Coral-Fort Myers, FLSource:Job Sprawl Stalls: The Great Recession and Metropolitan Employment Location, Metropolitan Policy Program, Brookings Institute. Elizabeth Kneebone. URL: https://www.brookings.edu/research/reports/2013/04/18-job-sprawl-kneebone

Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear response fashion. This nonlinear temporal change is consistent with the theories in boundary layer climatology that describe and explain the leading edge transition and energy balance theory. This pattern of urban vs. rural temperature response has been demonstrated in relation to spatial range of city sizes (using population data) for 305 rural vs. urban climate stations in the U.S. Our recent work on the two urban LTER sites has shown that a similar climate response pattern also occurs over time for climate stations that were initially located in rural locations have been overrun bu the urban fringe and subsequent urbanization (e.g., stations in Baltimore, Mesa, Phoenix, and Tempe). Lack of substantial numbers of weather and climate stations in cities has previously precluded small-scale analyses of geographic variations of urban climate, and the links to land-use change processes. With the advent of automated weather and climate station networks, remote-sensing technology, land-use history, and the focus on urban ecology, researchers can now analyze local climate responses as a function of the details of land-use change. Therefore, the basic research question of this study is: How does urban climate change over time and space at the place of maximum disturbance on the urban fringe? Hypotheses 1. Based on the leading edge theory of boundary layer climate change, largest changes should occur during the period of peak development of the land when land is being rapidly transformed from open desert and agriculture to residential, commercial, and industrial uses. 2. One would expect to observe, on average and on a temporal basis (several years), nonlinear temperature and humidity alterations across the station network at varying levels of urban development. 3. Based on past research on urban climate, one would expect to see in areas of the urban fringe, rapid changes in temperature (increases at night particularly), humidity (decreases in areas from agriculture to urban; increases from desert to urban), and wind speed (increases due to urban heating). 4. Changes of the surface climate on the urban fringe are expected to be altered as a function of various energy, moisture, and momentum control parameters, such as albedo, surface moisture, aerodynamic surface roughness, and thermal admittance. These parameters relate directly to population and land-use change (Lougeay et al. 1996).

The two metropolitan areas with the highest value of new residential construction in 2024 were in Texas. Those two areas, Dallas-Fort Worth-Arlington and Houston-Pasadena-The Woodlands, were the only ones in the United States where new homes authorized were worth over 17 billion U.S. dollars. Those figures were significantly higher than the following entries in the list, the areas around Phoenix (Arizona) and in New York, where home construction amounted to over ten billion U.S. dollars.

This data was created as part of a study that examined the accessibility to jobs by walking in the 53 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by walking, and it allows for a direct comparison of the walking accessibility performance of America's largest metropolitan areas. Downloads are available for individual metropolitan regions in CSV or Shapefile format. Combined ZIP files containing the data for all metropolitan regions are also available in CSV and Shapefile format, and are labeled as 'All Metropolitan Regions'.

In 2023, El Centro in California had the highest unemployment rate of any metro area at 17.3 percent unemployment. Yuma metro area in Arizona had the second-highest unemployment rate. 11 out of the top 20 areas by unemployment rate were in California.

Safe, reliable, and equitable water access is critical to human health and livelihoods. In this study, we undertake the first systematic and comprehensive analysis of household piped water access in the United States, with the aim of explaining drivers of infrastructural inequality in the 50 largest metropolitan areas. Drawing on statistical analysis and regression modeling of U.S. census microdata at the household scale, our analysis reveals spatial and sociodemographic patterns of racialized, class-based, and housing disparities that characterize plumbing poverty across metropolitan areas.This dataset includes relevant supplemental data for our manuscript titled, "Geographies of Insecure Water Access and the Housing-Water Nexus in U.S. Cities" (forthcoming, PNAS). Here, we present customized Public Use Microdata Sample (PUMS) definitions used in our study that make U.S. Metropolitan Statistical Area (MSA) geographies comparable over time, as well as the accompanying R code for statistical analysis of census microdata and the creation of spatial visualizations. Parties interested in collaborating on use of the full script may contact the corresponding author (K. Meehan).If you use this dataset or code, please cite as follows:Meehan, Katie; Jason R. Jurjevich; Nicholas M.J.W. Chun, and Justin Sherrill (2020): Metropolitan Geographic Definitions and Code for "Geographies of Insecure Water Access and the Housing-Water Nexus in U.S. Cities." Tucson, AZ: University of Arizona Research Data Repository. https://doi.org/10.25422/azu.data.12456536For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Safe, reliable, and equitable water access is critical to human health and livelihoods. In this study, we present the first longitudinal analysis of household access to running water—a vital social infrastructure—in the 50 largest US cities since 1970. In the accompanying paper published in Nature Cities, results of the analysis indicate that water access has worsened in an increasing number and typology of US cities since the 2008 global financial crash, disproportionately affecting households of color. We provide evidence to suggest that a ‘reproductive squeeze’—systemic, compounding pressures on households’ capacity to reproduce themselves on a daily and societal basis—is forcing urban households into more precarious living arrangements, including housing without running water, with few signs of abating.This file—which is the supplementary data that underpins the paper—contains the microdata dataset for the manuscript "Urban Inequality, the Housing Crisis and Deteriorating Water Access in US Cities" (Nature Cities). Here, we present customized and improved Public Use Microdata Sample (PUMS) definitions used in our study that enable researchers to compare US Metropolitan Statistical Area (MSA) over time, while minimizing spatial error. The dataset also includes accompanying R code for statistical analysis of census microdata and the creation of static and dynamic spatial visualizations.Parties interested in collaborating on use of the full script may contact the corresponding author (K. Meehan).If you use this dataset or code, please cite as follows: Meehan, Katie, Jason R. Jurjevich, Lucy Everitt, Nicholas M.J.W. Chun, and Justin Sherrill. (2024). Metropolitan Geographic Definitions and Code for "Urban Inequality, the Housing Crisis and Deteriorating Water Access in US Cities.” Tucson, AZ: University of Arizona Research Data Repository. DOI: 10.25422/azu.data.25724286FUNDINGThis research and dataset were supported by a grant selected by the European Research Council and funded by UKRI Horizon Europe Guarantee (Grant No. EP/Y024265/1)For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Tap waters were collected from major metropolitan areas of the western United States. Tap waters were sampled between 2012-2015 from seven metropolitan areas: Los Angeles-Long Beach-Santa Ana (CA), Phoenix-Mesa-Glendale (AZ), Salt Lake City (UT), San Diego-Carlsbad-San Marcos (CA), San Francisco-Oakland-Fremont (CA), San Jose-Sunnyvale-Santa Clara (CA), and Riverside-San Bernardino-Ontario (CA). These areas represent some of the most populous in the US and employ a diversity of water management practices. Here hydrogen (d2H) and oxygen (d18O) isotope values along with strontium isotope ratios (87Sr/86Sr) and element abundances were measured. d2H and d18O of 2039 tap waters were measured following Tipple et al., 2017 (Water Research, 119, 212-224). 87Sr/86Sr and elemental compositions of 820 and 806 waters were analyzed following Tipple et al., 2018 (Scientific Reports, 8, 2224), respectively. The purpose of these data was to assess spatial, temporal, and climatic dynamics in isotope and elemental geochemistry of tap waters. We found that the isotope and elemental geochemistry of tap waters corresponded to the water sources (e.g., transported water, local surface water, groundwater, etc.) and management practices (e.g., storage in open reservoirs, mixing, etc.) for discrete areas within the larger metropolitan areas.

About this dataset

Context

The dataset tabulates the Phoenix population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Phoenix across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Phoenix was 1.65 million, a 0.38% increase year-by-year from 2022. Previously, in 2022, Phoenix population was 1.64 million, an increase of 1.15% compared to a population of 1.63 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Phoenix increased by 322,874. In this period, the peak population was 1.68 million in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Phoenix is shown in this column.
• Year on Year Change: This column displays the change in Phoenix population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Phoenix Population by Year. You can refer the same here

Between 2015 and 2017, Memphis, Tennessee had the highest eviction rate at *** percent. The metropolitan areas with the next highest eviction rates were Phoenix (Arizona), Atlanta (Georgia), Indianapolis (Indiana) and Dallas (Texas) in that period.

Why do evictions occur? Eviction rate refers to the share of renters who are legally removed from a rental property by their landlord, because rent is overdue, the tenant has breached a condition of the rental agreement or for other legally permitted reasons.

Higher rates in the South and Midwest Eviction rates tend to be higher in the South and Midwest of the country, because median incomes are low and foreclosure rates are high. Vacancy rates are consistently higher in the South and Midwest than in the Northeast and West, which means that landlords cannot afford to be as picky when choosing a tenant in the South and Midwest. Tenants who struggle to pay their rent have a much lower chance of being chosen as tenant in the more competitive rental markets, which also keeps the eviction rates lower in those areas.

Plan submitted by: LGTrader on 09/22/2021 USER DESCRIPTION: 1) In this map the Phoenix metro area encompasses 5 congressional districts. The area was chosen using roughly the largest cities in Maricopa County and a total population of roughly 5*794611 or 3,973,055 people. USER PLAN OBJECTIVE: N/A

Animals utilize their environment across a range of scales, which is bounded by their extent, the broadest spatial area which organisms respond to their environment within their lifetime, and the spatial grain, the smallest area they respond to their environment (Kotlier and Wiens 1990). Within this range, organisms likely respond to their environment at a hierarchy of levels. Johnson (1980) recognizes four distinct levels of hierarchical habitat selection. At the very largest scale, first order selection, includes the entire area that an organism utilizes within its lifetime, and is also known as an organisms global home range or extent. In contrast, second order selection is an organisms local home range, or the area that it occupies within a unique ecosystem. This distinction is most apparent with migratory animals who utilize more than one distinct landscape for their survival (i.e. summer vs. winter feeding grounds), and much less so for organisms resident of one specific landscape for their entire life span. Third order selection is the selection of specific habitat patches within an ecosystem. For example, a Monarch butterfly would tend to select patches of milkweed within a prairie. And the lowest level, fourth order selection, involves the physical procurement of food within a selected patch, in our example, specific flowers within a milkweed patch, and is also known as grain. Realizing the importance of hierarchical habitat selection, it has become apparent that single-scale studies of animals responses to their environment may fail to adequately represent how that specific animal is responding to ecological parameter of interest, especially if they are not responding to the landscape at that scale (Holling 1992). The range of scales which an animal of interest is utilizing a landscape is important to determine prior to any further ecological investigation, as inappropriate scalar mismatch between organism and environment can lead to ambiguous or even deceptive conclusions. To do this, we compared the correlation coefficients of bird abundances for different functional groups (e.g. foraging guilds, natives vs. exotics) with vegetation cover, as a proxy for habitat, across a range of scales (from 100m to 10km). Theoretically, a unimodal (hump-shaped) relationship should exist for the correlation coefficients across a range of scales, under the assumption that vegetation cover is an adequate estimate of bird abundance. The peak of that relationship, if statistically significant, would represent the strongest correlation between habitat and bird abundance, and thus signifies the average third order selection unit for that group. A strong peak is expected for species directly dependent on vegetation for food (herbivores), a weaker peak for omnivores, and the weakest relationship for those species indirectly dependent on vegetation (insectivores). The regional distributional patterns of the varying bird functional groups was also estimated by utilizing interpolation techniques designed for avian censuses in urban systems. Exotic species were expected to be spatially aligned to the urban ecosystem, and native species tied to the desert ecosystem. Herbivores were expected to exist in higher densities were vegetation is greatest, which typically exists within the city and agricultural fields in arid ecosystems. The ongoing project (since October 2000) is documenting the abundance and distribution of birds in four habitats (51 sites): Urban (18) Desert (15) Riparian (11) and agricultural (7). The 40 non-riparian sites are a subset of the 200 CAP- LTER points. We are using point counts to survey birds four times a year (January, April, July and October). During each session each point is visited by three birders who count all birds seen or heard for 15 minutes. Our goal is to study how different land-use forms affect bird abundance, distribution and diversity in the grea... Visit https://dataone.org/datasets/knb-lter-cap.394.7 for complete metadata about this dataset.

The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization, water, and vegetation. We deployed 43 wildlife cameras across the gradient of urbanization January 2021 to January 2022. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.

The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization. We deployed 50 wildlife cameras across the gradient of urbanization from downtown Phoenix to nearby wildland areas from January 2019 to August 2020. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.

About this dataset

Context

The dataset tabulates the population of Phoenix by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Phoenix. The dataset can be utilized to understand the population distribution of Phoenix by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Phoenix. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Phoenix.

Key observations

Largest age group (population): Male # 30-34 years (69,724) | Female # 25-29 years (67,759). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Phoenix population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Phoenix is shown in the following column.
• Population (Female): The female population in the Phoenix is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Phoenix for each age group.

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 Phoenix Population by Gender. You can refer the same here

This dataset contain the parcels from the Maricopa County Assessor's office. The area covered is in Maricopa County (Phoenix metro area), Arizona. Major cities include Phoenix, Scottsdale, Mesa and Tempe. Various types of property usage are depicted in this layer. The most common are residential, commercial, industrial and agricultural properties. Multiple sources were used to collect the information including but not limitied to CAD packages, aerial photography, and digitizing from paper maps. Adjustments are made where necessary in the process of updating and some parcel lines are only approximate. In cases where a line adjustment might create a loss of landsize it is typically taken out from the right of way to minimize the loss of landsize in the property. The line symbology for different property lines are not available in this dataset. In addition to standard fields the data has unique assessor parcel number for identification, basic temporal information and the location of property.

Graph and download economic data for All-Transactions House Price Index for Phoenix-Mesa-Chandler, AZ (MSA) (ATNHPIUS38060Q) from Q2 1977 to Q1 2025 about Phoenix, AZ, appraisers, HPI, housing, price index, indexes, price, and USA.