In 2021, Philadelphia, Pennsylvania was the city with the highest poverty rate of the United States' most populated cities. In this statistic, the cities are sorted by poverty rate, not population. The most populated city in 2021 according to the source was New York city - which had a poverty rate of 18 percent.

In 2021, the city of Philadelphia in Pennsylvania had the highest family poverty rate of the 25 most populated cities in the United States. The city with the next highest poverty rate was Houston, Texas.

The McAllen-Edinburg-Mission metropolitan area in Texas was ranked first with 27.2 percent of its population living below the poverty level in 2023. Eagle Pass, Texas had the second-highest poverty rate, at 24.4 percent.

In 2021, New York city had the highest number of people living below the poverty line, with 1.4 million people living in poverty. This is significantly higher than any of the other most populated cities.

The poly shapefile is a subset of data that was derived from HRSA data that shows medically underserved ares within each county. Out of nearly 2,650 counties with such areas, there are only 96 counties that have areas that have incomes that are below the poverty level. The top five counties that are medically underserved and are poor are: 1. Radford, VA (52.7% pop below poverty level) 2. Autauga, AL (51.3% pop below poverty level) 3. Baldwin county, AL (50.2% pop below poverty level) 4. Washington D.C., (41.5% pop below poverty level) 5. Montgomery County, VA (40.4% pop below poverty level)

Data show the ratio of the income or expenditure share of the richest group (10%)to that of the poorest 10%

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.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

The gross domestic product (GDP) of California was about 4.1 trillion U.S. dollars in 2024, meaning that it contributed the most out of any state to the country’s GDP in that year. In contrast, Vermont had the lowest GDP in the United States, with 45.71 billion U.S. dollars. What is GDP? Gross domestic product, or GDP, is the total monetary value of all goods and services produced by an economy within a certain time period. GDP is used by economists to determine the economic health of an area, as well as to determine the size of the economy. GDP can be determined for countries, states and provinces, and metropolitan areas. While GDP is a good measure of the absolute size of a country's economy and economic activity, it does account for many other factors, making it a poor indicator for measuring the cost or standard of living in a country, or for making cross-country comparisons. GDP of the United States The United States has the largest gross domestic product in the world as of 2023, with China, Japan, Germany, and India rounding out the top five. The GDP of the United States has almost quadrupled since 1990, when it was about 5.9 trillion U.S. dollars, to about 25.46 trillion U.S. dollars in 2022.

Guyana was the South American country 20360the highest gross national income per capita, with 20,360 U.S. dollars per person in 2023. Uruguay ranked second, registering a GNI of 19,530 U.S. dollars per person, based on current prices. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Which are the largest Latin American economies? Based on annual gross domestic product, which is the total amount of goods and services produced in a country per year, Brazil leads the regional ranking, followed by Mexico, Argentina, and Chile. Many Caribbean countries and territories hold the highest GDP per capita in this region, measurement that reflects how GDP would be divided if it was perfectly equally distributed among the population. GNI per capita is, however, a more exact calculation of wealth than GDP per capita, as it takes into consideration taxes paid and income receipts from abroad. How much inequality is there in Latin America? In many Latin American countries, more than half the total wealth created in their economies is held by the richest 20 percent of the population. When a small share of the population concentrates most of the wealth, millions of people don't have enough to make ends meet. For instance, in Brazil, about 5.32 percent of the population lives on less than 3.2 U.S. dollars per day.

The Nigerian states of Sokoto and Taraba had the largest percentage of people living below the poverty line as of 2019. The lowest poverty rates were recorded in the South and South-Western states. In Lagos, this figure equaled 4.5 percent, the lowest rate in Nigeria.

A large population in poverty

In Nigeria, an individual is considered poor when they have an availability of less than 137.4 thousand Nigerian Naira (roughly 334 U.S. dollars) per year. Similarly, a person having under 87.8 thousand Naira (about 213 U.S. dollars) in a year available for food was living below the poverty line according to Nigerian national standards. In total, 40.1 percent of the population in Nigeria lived in poverty.

Food insecurity on the rise

On average, 21.4 percent of the population in Nigeria experienced hunger between 2018 and 2020. People in severe food insecurity would go for entire days without food due to lack of money or other resources. Over the last years, the prevalence with severe food among Nigerians has been increasing, as the demand for food is rising together with a fast-growing population.

In 2024, the annual per capita gross domestic product (GDP) in different provinces, municipalities, and autonomous regions in China varied from approximately 228,200 yuan in Beijing municipality to roughly 52,800 yuan in Gansu province. The average national per capita GDP crossed the threshold of 10,000 U.S. dollars in 2019 and reached around 95,700 yuan in 2024. Regional economic differences in China The level of economic development varies considerably in different parts of China. Four major geographic and economic regions can be discerned in the country: The economically advanced coastal regions in the east, less developed regions in Northeast and Central China, and the developing regions in the west. This division has deep historical roots reflecting the geography of each region and their political past and present. Furthermore, regional economic development closely correlates with regional urbanization rates, which closely resembles the borders of the four main economic regions. Private income in different parts of China Breaking the average income figures further down by province, municipality, or autonomous region reveals that the average disposable income in Shanghai or Beijing is on average more than three times higher than in Tibet or Gansu province. In rural areas, average disposable income is often only between one third and one half of that in urban areas of the same region. Accordingly, consumer expenditure per capita in urban areas reaches the highest levels in Shanghai, Beijing, and the coastal regions of China.