This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This dataset contains different collected datasets with crime data of many large cities. Below are the descriptions for each seperate dataset. Note: Dataset properties and column may differ from each other since the information was collected by the local police in different styles and situations.

1. Los Angeles

The Los Angeles dataset has the collected data on different crimes that happened in Los Angeles from 2000 up until May 2024. The columns are as follows:

• DR_NO - Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits

• Date Rptd - The date when the police found out about the crime

• Date OCC - The actual date of the crime

• Time OCC - In military time

• Area - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.

• Area Name - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South Broadway and 77th Street, serving neighborhoods in South Los Angeles.

• Rpt Dist No - A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the "RD" that it occurred in for statistical comparisons. Find LAPD Reporting Districts on the LA City GeoHub at http://geohub.lacity.org/datasets/c4f83909b81d4786aa8ba8a74a4b4db1_4

• Crm Cd - Indicates the crime committed. (Same as Crime Code 1)

• Crm Cd Desc - Defines the Crime Code provided.

• Mocodes - Modus Operandi: Activities associated with the suspect in commission of the crime.

• Vict Age - The age of the victim

• Vict Sex - The gender of the victim. They are as follows:

  • M - Male
  • F - Female
  • X - Unknown

• Vict Descent - Descent Code:

  • A - Other Asian
  • B - Black
  • C - Chinese
  • D - Cambodian
  • F - Filipino
  • G - Guamanian
  • H - Hispanic/Latin/Mexican
  • I - American Indian/Alaskan Native
  • J - Japanese
  • K - Korean
  • L - Laotian
  • O - Other
  • P - Pacific Islander
  • S - Samoan
  • U - Hawaiian
  • V - Vietnamese
  • W - White
  • X - Unknown
  • Z - Asian Indian

• Premis Cd - The type of structure, vehicle, or location where the crime took place.

• Premis Desc - Defines the Premise Code provided.

• Weapon Used Cd - The type of weapon used in the crime.

• Status - Status of the case. (IC is the default)

• Status Desc - Defines the Status Code provided.

• Crm Cd 1 - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.

• Crm Cd 2 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 3 - May contain a code for an additional crime, less serious than Crime Code 1.

• Crm Cd 4 - May contain a code for an additional crime, less serious than Crime Code 1.

• Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.

• Cross Street - Cross Street of rounded Address

• LAT - Latitude

• LON - Longitude

This dataset has 28 columns and 944K rows. I hope you will find it useful. God bless you

1. Chicago

This dataset contains crime data on Chicago, from 2001 to present. The columns are as follows:

• ID - Unique Identifier for the record

• Case Number - The Chicago Police Department RD Number (Records Division Number), which is unique to the incident.

• Date - Date when the incident occurred. this is sometimes a best estimate.

• Block - The partially redacted address where the incident occurred, placing it on the same block as the actual address.

• IUCR - The Illinois Unifrom Crime Reporting code. This is directly linked to the Primary Type and Description. See the list of IUCR codes at https://data.cityofchicago.org/d/c7ck-438e..

• Primary Type - The primary description of the IUCR code.

• Description - The secondary description of the IUCR code, a subcategory of the primary description.

• Location Description - Description of the location where the incident occurred.

• Arrest - Indicates whether an arrest was made.

• Domestic - Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act.

• Beat - Indicates the beat where the incident occurred. A beat is the smallest police geographic area – each beat has a dedicated police beat car. Three to five beats make up a police sector, and three sectors make up a police district. The Chicago Police Department has 22 police districts. See the beats at https://data.cityofchicago.org/d/aerh-rz74.

• Distric...

Among the 81 largest metropolitan areas (by population) in the United States, Knoxville, Tennessee was ranked first with **** percent of residents reporting as white, non-Hispanic in 2023.

GIS Web Map Application of the 10 City Council Voter Districts

Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=5,660,237 trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems. Methods See eLife manuscript for full details. Below, we provide a summary of how the dataset was collected and processed.

Data Acquisition We limited our search to the 150 largest cities in the USA (by census population). To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning All code used is in the zipped folder Data S5 in the eLife publication. Before cleaning the data, we ensured that all reported trees for each city were located within the greater metropolitan area of the city (for certain inventories, many suburbs were reported - some within the greater metropolitan area, others not). First, we renamed all columns in the received .csv sheets, referring to the metadata and according to our standardized definitions (Table S4). To harmonize tree health and condition data across different cities, we inspected metadata from the tree inventories and converted all numeric scores to a descriptive scale including “excellent,” “good”, “fair”, “poor”, “dead”, and “dead/dying”. Some cities included only three points on this scale (e.g., “good”, “poor”, “dead/dying”) while others included five (e.g., “excellent,” “good”, “fair”, “poor”, “dead”). Second, we used pandas in Python (W. McKinney & Others, 2011) to correct typos, non-ASCII characters, variable spellings, date format, units used (we converted all units to metric), address issues, and common name format. In some cases, units were not specified for tree diameter at breast height (DBH) and tree height; we determined the units based on typical sizes for trees of a particular species. Wherever diameter was reported, we assumed it was DBH. We standardized health and condition data across cities, preserving the highest granularity available for each city. For our analysis, we converted this variable to a binary (see section Condition and Health). We created a column called “location_type” to label whether a given tree was growing in the built environment or in green space. All of the changes we made, and decision points, are preserved in Data S9. Third, we checked the scientific names reported using gnr_resolve in the R library taxize (Chamberlain & Szöcs, 2013), with the option Best_match_only set to TRUE (Data S9). Through an iterative process, we manually checked the results and corrected typos in the scientific names until all names were either a perfect match (n=1771 species) or partial match with threshold greater than 0.75 (n=453 species). BGS manually reviewed all partial matches to ensure that they were the correct species name, and then we programmatically corrected these partial matches (for example, Magnolia grandifolia-- which is not a species name of a known tree-- was corrected to Magnolia grandiflora, and Pheonix canariensus was corrected to its proper spelling of Phoenix canariensis). Because many of these tree inventories were crowd-sourced or generated in part through citizen science, such typos and misspellings are to be expected. Some tree inventories reported species by common names only. Therefore, our fourth step in data cleaning was to convert common names to scientific names. We generated a lookup table by summarizing all pairings of common and scientific names in the inventories for which both were reported. We manually reviewed the common to scientific name pairings, confirming that all were correct. Then we programmatically assigned scientific names to all common names (Data S9). Fifth, we assigned native status to each tree through reference to the Biota of North America Project (Kartesz, 2018), which has collected data on all native and non-native species occurrences throughout the US states. Specifically, we determined whether each tree species in a given city was native to that state, not native to that state, or that we did not have enough information to determine nativity (for cases where only the genus was known). Sixth, some cities reported only the street address but not latitude and longitude. For these cities, we used the OpenCageGeocoder (https://opencagedata.com/) to convert addresses to latitude and longitude coordinates (Data S9). OpenCageGeocoder leverages open data and is used by many academic institutions (see https://opencagedata.com/solutions/academia). Seventh, we trimmed each city dataset to include only the standardized columns we identified in Table S4. After each stage of data cleaning, we performed manual spot checking to identify any issues.

This data package consists of 26 datasets all containing statistical data relating to the population and particular groups within it belonging to different countries, mostly the United States.

Houston had almost *** times more Bitcoin ATMs than Baltimore in November 2025, with nearly *** cryptocurrency installations found in the latter city. In general, Bitcoin ATMs were not necessarily found in the bigger cities of the United States: Philadelphia, for instance, counted fewer machines than Detroit or Orlando. These ATMs are different from traditional cash machines in that they do not connect to a bank account but connect users to a Bitcoin wallet or exchange. This way, they can convert physical money into digital currency. The United States housed the highest amount of these machines worldwide.

Where should we live in the next 10 years? Where should we settle down without relying on public transport? Which city should we move to without fearing losing our homes?

As weather patterns become more unpredictable with aggressive changes in temperatures, I collected some data below to see if there would be a city that could help assess our answers to the prior questions. I am curious to see if cities that typically have great infrastructure for walking, biking or public transit will be better prepared than those that are more typically car centric. Whichever you prefer, we can have a sense on where you might be migrating, and to which areas.

Here's how the data was collected:

1. Rhodium & ProPublica's combined work on counties risk factors against climate change across continental US (excludes Hawaii, Alaska, Puerto Rico and Guam. Washington D.C, is excluded as it does not have a county.)
2. The available Walk Score of major cities that have a population above 100,000 represented. Cities like Delaware's Wilmington or Maine's Portland are not considered as it falls under a small-city definition
3. Maximum temperatures (for select cities): This dataset is collected from the UC Davis Department of Agricultural and Resource by Prof. Aaron Smith. I selected a monthly temporal unit and county spatial unit ranging from 2019 - 2024 July. This dataset is extracted based on the average of highest temperatures in each selected counties. I did not use the overall daily average as it can easily shadow the extremities of temperature fluctuations.

The columns have different rating systems. The counties have all major climate risks expected in the future, while corresponding cities in each county have walking, transit and biking scores to assess livability without cars.

Understanding County Climate Risks The counties were were represented on a 1- 10 scale, based on RCP 8.5 levels. Here are the following explanations (0 = lowest, 10 = highest)

1) Heat: Heat is one of the largest drivers changing the niche of human habitability. Rhodium Group researchers estimate that, between 2040 and 2060 extreme temperatures, many counties will face extremely high temperatures for half a year. The measure shows how many weeks per year will we anticipate temperatures to soar above 95 degrees. (0 = 0 weeks, 10 = 26 weeks).

2) Wet Bulb: Wet bulb temperatures occur when heat meets excessive humidity. This is commonplace across cities that have a urban island heat effects (dense concentration of pavements, less nature, higher chances of absorbing heat). That combination creates wet bulb temperatures, where 82 degrees can feel like southern Alabama on its hottest day, making it dangerous to work outdoors and for children to play school sports. As wet bulb temperatures increase even higher, so will the risk of heat stroke — and even death. The measure shows how many days will a county experience high wet bulb temperatures yearly, from 2040 to 2060. (0 = 0 days, 10 = 70 days)

3) Farm Crop Yield: With rising temperatures, it will become more difficult to grow food. Corn and soy are the most prevalent crops in the U.S. and the basis for livestock feed and other staple foods, and they have critical economic significance. Because of their broad regional spread, they offer the best proxy for predicting how farming will be affected by rising temperatures and changing water supplies. As corn and soy production gets more sensitive to heat than drought, the US will see a huge continental divide between cooler counties now having more ability to produce, while current warmer counties loosing all abilities to produce basic crops. The expected measure shows the percent decline yields from 2040 to 2060 (0 = -20.5% decline, 10 = 92% decline).

4) Sea Level Rise: As sea levels rise, the share of property submerged by high tides increases dramatically, affecting a small sliver of the nation's land but a disproportionate share of its population. The rating measures how much of property in the county will go below high tide from 2040 to 2060 (0 = 0%, 10 = 25%).

5) Very Large Fires: With heat and evermore prevalent drought, the likelihood that very large wildfires (ones that burn over 12,000 acres) will affect U.S. regions increases substantially, particularly in the West, Northwest and the Rocky Mountains. The rating calculates how many average number of large fires will we expect to see per year (0 = N/A, 10 = 2.45) from 2040 to 2071.

6) Economic Damages: Rising energy costs, lower labor productivity, poor crop yields and increasing cr...

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

How many incorporated places are registered in the U.S.?

There were 19,502 incorporated places registered in the United States as of July 31, 2019. 16,410 had a population under 10,000 while, in contrast, only 10 cities had a population of one million or more.

Small-town America

Suffice it to say, almost nothing is more idealized in the American imagination than small-town America. When asked where they would prefer to live, 30 percent of Americans reported that they would prefer to live in a small town. Americans tend to prefer small-town living due to a perceived slower pace of life, close-knit communities, and a more affordable cost of living when compared to large cities.

An increasing population

Despite a preference for small-town life, metropolitan areas in the U.S. still see high population figures, with the New York, Los Angeles, and Chicago metro areas being the most populous in the country. Metro and state populations are projected to increase by 2040, so while some may move to small towns to escape city living, those small towns may become more crowded in the upcoming decades.

Percent Sunshine by US City

Discovering the Clear Skies for Astronomy and Sunniest Locations in the US

By Gary Hoover [source]

About this dataset

This dataset provides all the information you need to plan a visit to a city that offers plenty of sunshine! With data on average annual percent of possible sunshine for US cities, you'll be able to answer questions like Which cities get the most sunlight on average each year?, What months are best for sunny days in [CITY]? and many more. Whether you're looking for somewhere warm and sunny to take your vacation, or clear skies for nighttime stargazing, this dataset has all the answers! Find out how each city in the US stacks up against each other and make sure your next trip is one filled with bright blue skies!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Creating a City Sunshine Index to compare the average percent of sunshine in different cities
• Estimating the amount of daylight hours available for astronomy stargazing in each city
• Creating a map to visualize monthly sunshine insights for vacation planning to different parts of the U.S

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Average Percent of Possible Sunshine by US City.csv | Column name | Description | |:--------------|:------------------------------------------------------------------| | CITY | Name of the city. (String) | | JAN | Average percent of possible sunshine in January. (Float) | | FEB | Average percent of possible sunshine in February. (Float) | | MAR | Average percent of possible sunshine in March. (Float) | | APR | Average percent of possible sunshine in April. (Float) | | MAY | Average percent of possible sunshine in May. (Float) | | JUN | Average percent of possible sunshine in June. (Float) | | JUL | Average percent of possible sunshine in July. (Float) | | AUG | Average percent of possible sunshine in August. (Float) | | SEP | Average percent of possible sunshine in September. (Float) | | OCT | Average percent of possible sunshine in October. (Float) | | NOV | Average percent of possible sunshine in November. (Float) | | DEC | Average percent of possible sunshine in December. (Float) | | ANN | Average percent of possible sunshine for the entire year. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Gary Hoover.

About this dataset

Context

The dataset presents median household incomes for various household sizes in California City, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, California City did not include 7-person households. Across the different household sizes in California City the mean income is $58,313, and the standard deviation is $24,712. The coefficient of variation (CV) is 42.38%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $28,660. It then further increased to $42,839 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/california-city-ca-median-household-income-by-household-size.jpeg" alt="California City, CA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

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

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 California City median household income. You can refer the same here

San Francisco, CA, New York, NY, and Jersey City, NJ were the most pedestrian friendly cities in the United States in 2021. The source analyzed the walking routes of different locations in the ** largest cities in the country to different amenities, as well as additional metrics, such as population density, block length, and intersection density. San Francisco, CA received **** index points, while the **** city in the ranking, St. Louis, MO, received **** index points.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

In 2025, the degree of urbanization worldwide was at 58 percent. North America, Latin America, and the Caribbean were the regions with the highest level of urbanization, with over four-fifths of the population residing in urban areas. The degree of urbanization defines the share of the population living in areas defined as "cities". On the other hand, less than half of Africa's population lives in urban settlements. Globally, China accounts for over one-quarter of the built-up areas of more than 500,000 inhabitants. The definition of a city differs across various world regions - some countries count settlements with 100 houses or more as urban, while others only include the capital of a country or provincial capitals in their count. Largest agglomerations worldwideThough North America is the most urbanized continent, no U.S. city was among the top ten urban agglomerations worldwide in 2023. Tokyo-Yokohama in Japan was the largest urban area in the world that year, with 37.7 million inhabitants. New York ranked 13th, with 21.4 million inhabitants. Eight of the 10 most populous cities are located in Asia. ConnectivityIt may be hard to imagine how the reality will look in 2050, with 70 percent of the global population living in cities, but some statistics illustrate the ways urban living differs from suburban and rural living. American urbanites may lead more “connected” (i.e., internet-connected) lives than their rural and/or suburban counterparts. As of 2021, around 89 percent of people living in urban areas owned a smartphone. Internet usage was also higher in cities than in rural areas. On the other hand, rural areas always have, and always will, attract those who want to escape the rush of the city.

New York, NY, Toronto, CA-ON, and San Franscisco, CA were the most pedestrian friendly cities in the United States in 2021. The source analyzed the walking routes of different locations in the ** largest cities in the country to different amenities, as well as additional metrics, such as population density, block length, and intersection density. New York, NY received **** index points, while the **** city in the ranking, Minneapolis, MN, received ** index points.

In 2023, California had the highest Hispanic population in the United States, with over 15.76 million people claiming Hispanic heritage. Texas, Florida, New York, and Illinois rounded out the top five states for Hispanic residents in that year. History of Hispanic people Hispanic people are those whose heritage stems from a former Spanish colony. The Spanish Empire colonized most of Central and Latin America in the 15th century, which began when Christopher Columbus arrived in the Americas in 1492. The Spanish Empire expanded its territory throughout Central America and South America, but the colonization of the United States did not include the Northeastern part of the United States. Despite the number of Hispanic people living in the United States having increased, the median income of Hispanic households has fluctuated slightly since 1990. Hispanic population in the United States Hispanic people are the second-largest ethnic group in the United States, making Spanish the second most common language spoken in the country. In 2021, about one-fifth of Hispanic households in the United States made between 50,000 to 74,999 U.S. dollars. The unemployment rate of Hispanic Americans has fluctuated significantly since 1990, but has been on the decline since 2010, with the exception of 2020 and 2021, due to the impact of the coronavirus (COVID-19) pandemic.

In 2021, the most popular cities in the U.S. for Gen Z renters had over ** percent share of Gen Z applicants. Boulder, CO had the highest share of Gen Z renter applicants of ** percent.

While these cities had the highest share of Gen Z apartment seekers in 2021, different cities registered a significant growth in the share of Gen Z applicants between 2020 and 2021. These cities are likely to join this list in the near future.

In 2021, the five most popular cities in Florida, U.S. for Gen Z renters had a large range of applications from this generation from ** percent in Tallahassee to ** percent in St. Petersburg.

Overall, the cities with the highest share of Gen Z apartment applicants in the United States were Boulder, CO, Davis, CA, and Conway, AR, all exceeding ** percent of apartment seekers. This can be explained with different factors such as the median age of the population, apartment availability, and the monthly rental cost.

The United States is home to many different types of beaches, from sand to rock, and even lava. Of course, some cities are home to more beaches than others. In 2022, Virginia Beach, Virginia, had the largest expanse of beaches with 8.2 miles of beach per 100,000 residents.