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.

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.

In 2023, around 27.4 percent of residents in Bremen were at risk of living in poverty. This list shows the 15 cities in Germany with the highest at-risk-of-poverty rates.

Per capita gross domestic product (GDP) of cities in China varies tremendously, mainly depending on the location of the city. Cities with the highest per capita GDP are mainly to be found in coastal provinces in East China and in South China, like Guangdong province. The poorest cities are located in the still less developed western parts of China, like Gansu province, or in the Chinese rust belt in Northeastern China, like Heilongjiang province.

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.

The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.

The municipalities of Manapiare and Bolívar, located in the Venezuelan states of Amazonas and Falcón, respectively, registered the highest share of population living under the poverty line in the country in 2021. That year, almost the entire population of these municipalities was reported to be living in poverty. All the 25 Venezuelan cities listed in this statistic had at least 99.7 percent of their population living under the poverty line.

This map shows demographic and income data in Detroit. Assuming an assignment where the poverty fighting charity I work for would like to alleviate suffering among impoverished children in Detroit. Detroit is a Michigan city that always ranks among America's poorest urban centers. Orange circles have below average median household income, the darker shades indicate households with a very low income-close to poverty level. The size of the circles: larger circles indicate a greater number of children in the area.What stands out is the obvioud pattern of low-income households in the city center combined with areas of high child population. This pattern helps answer where in Detroit our charity will focus its resources to help children living in poverty-in places shown on the map where there is a cluster of several large dark Orange circles like Dearborn and Pontiac (for example). The charity may and will offer free after school care and/Or but not limited to breakfast programs.

This map shows the percent of adults 18+ who report 14 or more days during the past 30 days during which their physical health was not good.As stated by the CDC in the methodology:Physical health is an important component of Health-related quality of life (HRQOL), a multi-dimensional concept that focuses on the impact of health status on quality of life.Who is included in this survey?Resident adults aged ≥18 years. Respondents aged ≥18 years who report or do not report the number of days during the past 30 days during which their physical health was not good (excluding those who refused to answer, had a missing answer, or answered “don’t know/not sure”).Data SourceCDC's 2017 500 Cities ProjectArcGIS Living Atlas of the World contains multiple years of 500 Cities CDC layers, which can be found here. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

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 study explored the lives of the working poor in the inner city. Three hundred male and female participants were drawn from central and west Harlem, New York City; 200 worked at one of four fast food restaurants in Harlem, and 100 had applied to one of those restaurants but were not hired. Participants were African American, Dominican and Puerto Rican of varied ages, most between 15 and 40 years of age. Educational status also varied, with the majority of participants' highest level of education being a high school degree. This study consists of three waves. The first wave was conducted in 1993-1994 with 300 participants. All 300 completed a survey, providing data on basic demographics (such as race, marital status, income, members of family, places where respondent has lived), as well as information on education, health care, and in-depth employment history. One-hundred fifty of these participants completed an extensive, semi-structured three to four hour interview telling their life history, covering topics such as family history; neighborhood identity; work history and aspirations; and race relations. Interviewers noted their impressions of the neighborhood and the physical appearance of the participant and her surroundings. The restaurant owners and managers were interviewed as well. Twelve of the participants agreed to be intensely studied; members of the research team worked alongside these participants at the fast food restaurants for four months, got to know their parents and children, and interviewed other key figures in their lives such as teachers and priests. The second wave was conducted in 1997-1998 with 100 of the original participants - some were employed, and some were unemployed. A survey was completed, addressing the same topics as the wave one survey. Interviews were conducted to ascertain life updates since wave one. The third wave was conducted in 2001-2002 with 40 of the 100 wave 2 participants. No more follow-up waves are planned. The Henry A. Murray Research Archives currently holds original record paper data, and audiotape data from waves 1 and 2 of this study.

This map app shows demographic and income data in Detroit. Assuming an assignment where the poverty fighting charity I work for would like to alleviate suffering among impoverished children in Detroit. Detroit is a Michigan city that always ranks among America's poorest urban centers. Orange circles have below average median household income, the darker shades indicate households with a very low income-close to poverty level. The size of the circles: larger circles indicate a greater number of children in the area.What stands out is the obvioud pattern of low-income households in the city center combined with areas of high child population. This pattern helps answer where in Detroit our charity will focus its resources to help children living in poverty-in places shown on the map where there is a cluster of several large dark Orange circles like Dearborn and Pontiac (for example). The charity may and will offer free after school care and/Or but not limited to breakfast programs.

This paper highlights the spatial linkages of forest quality with poverty incidence and poverty density in Vietnam. Most of the Vietnamese poor live in densely populated river deltas and cities while remote upland areas have the highest poverty incidences, gaps, and severities. Forests of high local and global value are located in areas where relatively few poor people live, but where the incidence, gap, and severity of poverty are strongest, and where the livelihood strategies are based on agricultural and forest activities. Analysis was conducted combining country-wide spatial data on commune-level poverty estimates and the geographic distribution of forest quality. The results suggest the usefulness of targeting investments in remote areas that combine poverty reduction and environmental sustainability.

Overview of the covariates (matching variables, effect moderators, and covariates) to be included in model.

OVERVIEW This dataset contains data from a survey of low income households in four cities across south India. This fileset includes a guidance document on how the data was collected and how to interpret and use the data. The survey data was collected between April-June 2019. A team of 11 survey enumerators and researchers were involved in the data collection which was collected through a collaboration between the University of Cambridge and the Indian Institute for Human Settlements. Data collection for this project received ethical approval from both the Department of Engineering, University of Cambridge and Indian Institute for Human Settlements. This anonymised dataset is being released to allow full use by others.

DATASET CONTENTS This dataset contains the following files: - Indian_Low_Income_Household_Energy_Survey_Codebook.pdf - south_indian_household_energy_survey_19.csv - south_indian_household_energy_survey_19.Rda - README.txt Data contained in the csv files is the same as data contained in the Rda file.

HOW TO USE All csv files can be opened using any appropriate software. Rdata script files must be opened and run using R. We recommend using RStudio and R version 3.5.1 (“Feather Spray”) or later.

This survey followed the same methodology and as an earlier survey of low-income households in Bangalore, India. The dataset from this earlier survey can be found at: https://doi.org/10.17863/CAM.59870

This dataset was used as external validation dataset for a microsimulation of cooking fuel use in India cities. Code for the microsimulation model can be found in the following GitHub repository: github.com/anetobradley/urban_energy_microsimulation_india

Politics and the Migrant Poor in Mexico City is a comparative study of male migrants and their city-born neighbors living in six relatively small, predominately low-income communities on the periphery of Mexico City. Based on 14 months of fieldwork in these communities during 1970, 1971, and 1972, this study dealt with a relatively small group of people in a limited number of localities at a particular point in time. The research addressed several broad theoretical and empirical problems such as the most important incentives and disincentives for political involvement, the effect a large group of people entering the political arena has on the functioning of the political system, how the individual citizen -- and especially the disadvantaged citizen -- can manipulate the political system to satisfy their needs, the process by which individuals form images of politics and the political system, the process by which individuals assume a role of participation or non-participation in political activity, what occurs at the "grass roots" of a nation's political system, and how political activity at that level affects system outputs. This study attempted to place the low-income migrant in a social and political context, and focused on the nature and frequency of interactions between the research communities and external actors, especially political and government officials. Demographic variables include age, race, socio-economic status, marital status, dwelling unit type, and religious preference.

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

This measure estimates the percentage of City infrastructure in poor or failing condition, based on departments participating in the City's Comprehensive Infrastructure Assessment.

This dataset was generated from a survey that studied newly-hired home health attendants and their families, using a pre- and post-program design. A total of 475 attendants were interviewed for the pre-program, and follow-up interviews were obtained from 360 of the 475 sample members. Most of the study subjects were without medical insurance until they became eligible for health benefits through their union. The attendants were interviewed at the point of union enrollment, and again nine months later to assess changes in health status and health services utilization.

During the pre-program survey, respondents were queried about concerns over health, recent injuring, self-assessed health, extent and type of previous health coverage, limitations of daily functioning due to poor health, and recent health care utilization. The post-program survey included questions about out-of-pocket expenses, type of health services received, and questions about health care utilization that were specific to the New York City area. Additional variables in the data collection include respondent's race, Hispanic origin, place of birth, past work experience, date of birth, and sex, plus the sex and dates of birth of family members.