Population growth drives increasing demand for housing, jobs, food, education, transportation and many services. Population decline is the flip side of that dynamic, creating its own pressures on local business, government, housing and people.This map shows which areas are under significant pressure from population growth or decline. As the population of the U.S. continues to grow, the cities and the suburbs are experiencing changes in their population density. This map shows areas of declining density in brown, and high growth in dark green.Red areas will lose population by 2015, while green areas will grow. Darker green areas will grow more than 1.25% per year. Click on the map for details about an area. Use this map as a backdrop for your organization's locations, services areas, or other subjects. There is also a simple app showing this web map.You candownload the data from this map package.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The COVID-19 crisis has impacted the lives of the entire nation. As city residents faced lockdowns, they turned to their public parks and open space for respite from the confines of city living. Many residents sought solace in natural areas, wishing to hike, bird, and experience the sights and sounds of a forest during this fraught time. To understand the impacts of the COVID-19 crisis on the public use of natural areas and organizations' ability to care for them, we deployed a survey in May of 2020 to known partners in 12 US cities that are leaders in the management and care of urban natural areas. These cities represent a combined population of over 18 million people and collectively manage 284,906 acres of natural area parkland. We found that most organizations (83%) reported an increase in use of natural areas but concurrently 72% reported a decrease in the ability to care for natural areas during the pandemic. All organizations reported canceled public programs, and 94% saw a decrease in volunteer events. As these organizations look to the future, only 17% were confident in their organization having adequate funding in 2021. Cutting budgets to care for urban natural areas could have significant impacts on the health and sustainability of urban life. These 12 cities serve as examples of a pattern that could be occurring nationally and internationally. As cities reopen, budgets and priorities for the future will be determined as will the fate of resources to care for nature in cities.
Prior to the arrival of European explorers in the Americas in 1492, it is estimated that the population of the continent was around sixty million people. Over the next two centuries, most scholars agree that the indigenous population fell to just ten percent of its pre-colonization level, primarily due to the Old World diseases (namely smallpox) brought to the New World by Europeans and African slaves, as well as through violence and famine.
Distribution
It is thought that the most densely populated region of the Americas was in the fertile Mexican valley, home to over one third of the entire continent, including several Mesoamerican civilizations such as the Aztec empire. While the mid-estimate shows a population of over 21 million before European arrival, one estimate suggests that there were just 730,000 people of indigenous descent in Mexico in 1620, just one hundred years after Cortes' arrival. Estimates also suggest that the Andes, home to the Incas, was the second most-populous region in the Americas, while North America (in this case, the region north of the Rio Grande river) may have been the most sparsely populated region. There is some contention as to the size of the pre-Columbian populations in the Caribbean, as the mass genocides, forced relocation, and pandemics that followed in the early stages of Spanish colonization make it difficult to predict these numbers.
Varying estimates Estimating the indigenous populations of the Americas has proven to be a challenge and point of contention for modern historians. Totals from reputable sources range from 8.4 million people to 112.55 million, and while both of these totals were published in the 1930s and 1960s respectively, their continued citation proves the ambiguity surrounding this topic. European settlers' records from the 15th to 17th centuries have also created challenges, due to their unrealistic population predictions and inaccurate methodologies (for example, many early settlers only counted the number of warriors in each civilization). Nonetheless, most modern historians use figures close to those given in the "Middle estimate" shown here, with similar distributions by region.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the California 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 California 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 2024, the population of California was 39.43 million, a 0.59% increase year-by-year from 2023. Previously, in 2023, California population was 39.2 million, an increase of 0.14% compared to a population of 39.14 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of California increased by 5.44 million. In this period, the peak population was 39.52 million in the year 2020. 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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for California Population by Year. You can refer the same here
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License information was derived automatically
We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped origin-destination networks from census block groups to points of interest (POIs), such as restaurants, museums, and schools, in sixteen cities in the United States. We adopted a coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visits. Sixteen cities are divided into two categories. In one category, which includes the cities of, San Francisco, Seattle, and Chicago, we observe a considerable decrease in hotspot POI visits, while in another category, including the cites of, Austin, Houston, and San Diego, the visits to hotspots did not greatly decrease. While all the cities exhibited overall decreasing visits to POIs, one category maintained the proportion of visits to hotspot POIs. The proportion of visits to some POIs (e.g., Restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visits (e.g., Grocery Stores). We also identified POIs with significant flux-in changes, showing that related businesses were greatly affected by social distancing.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Ohio City 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 Ohio City 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 Ohio City was 641, a 0.47% decrease year-by-year from 2022. Previously, in 2022, Ohio City population was 644, a decline of 0.16% compared to a population of 645 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Ohio City decreased by 173. In this period, the peak population was 814 in the year 2000. 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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Ohio City Population by Year. You can refer the same here
This map shows the historical housing unit change in consistent 2010 census tract boundaries from 1940 to 2019. In many cities over that time period—especially in the 1950s and 1960s—federal, state, and local governments demolished thousands of housing units as part of their "urban renewal" programs. These neighborhoods were typically in the older parts of city centers, contained lower income populations, and had higher shares of Black, Hispanic, and immigrant residents than their respective cities. Homes were typically replaced with new interstate highways and thoroughfares, stadiums, civic buildings, parking lots, high rises, rights of way, and other non-residential uses. In a fraction of cases, homes were replaced with public housing. Many of these areas show up as red on this map because they still have not regained the level of housing they had before World War II.Urban renewal is not the only reason for housing loss. Many tracts in places that have been undergoing population decline—especially cities in the North and Midwest and many rural communities—have also lost considerable amounts of housing over this time period.On the other side of things, many suburban and exurban areas—especially in the South and West—have experienced significant population and housing unit growth. These places show up as blue on this map.The data used to make this map comes from the Historical Housing Unit and Urbanization Database 2010, or HHUUD10. To read more on the methodologies used to estimate the housing unit counts, please refer to the methods paper. To download the data in tabular form, please visit the data repository. To download the feature layer used to make this map or read about the attributes, see the feature layer. Please also remember that these data are estimates and are therefore imperfect. They should be treated like all interpolated data: with caution and a healthy dose of skepticism.Citation:Markley, S.N., Holloway, S.R., Hafley, T.J., Hauer, M.E. 2022. Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940–2019. Scientific Data 9 (82). https://doi.org/10.1038/s41597-022-01184-x
In 2022, the Detroit metro area GDP amounted to ****** billion U.S. dollars, an increase from the previous year. Detroit's GDP Between 2001 and 2022, the GDP of the Detroit-Warren-Dearborn metro area rose from ****** billion U.S. dollars in 2001 to ****** billion U.S. dollars in 2021, dipping in 2009 to ****** billion U.S. dollars. Despite a rise in GDP, the city of Detroit filed for bankruptcy in July 2013 with debts of approximately ** billion U.S. dollars. Detroit was the largest municipality to file for bankruptcy since 1953. Second largest was Jefferson County, Alabama, which filed in 2011 with debts of approximately *** billion U.S. dollars. In 2021, the Detroit metro area had a population of around 4.36 million inhabitants. City of Detroit Detroit was once a major production hub of the American automobile industry, but has since suffered decline as car manufacturers faced international competition and automobile production was moved out of the city. As a result, workers left Detroit and the population fell. In 2019, Detroit had a resident population of roughly ******* people, ranking **** on the list of largest U.S. cities, but has since fallen off the list of the ** most populous cities in the U.S. Poverty remains a problem for the city and many buildings remain empty and derelict. Crime rates also indicate the extent of Detroit’s decline. Detroit was the second most dangerous city in America in 2022, with ***** crimes per 100,000 residents.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Baltimore city 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 Baltimore city 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 Baltimore city was 565,239, a 0.68% decrease year-by-year from 2022. Previously, in 2022, Baltimore city population was 569,107, a decline of 1.30% compared to a population of 576,578 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Baltimore city decreased by 83,507. In this period, the peak population was 648,746 in the year 2000. 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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Baltimore city Population by Year. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 –August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June—August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Jersey City 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 Jersey City 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 Jersey City was 291,657, a 0.65% increase year-by-year from 2022. Previously, in 2022, Jersey City population was 289,772, an increase of 1.64% compared to a population of 285,105 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Jersey City increased by 51,565. In this period, the peak population was 291,949 in the year 2020. 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).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Jersey City Population by Year. You can refer the same here
In 2023, the state with the highest crime rate in the United States per 100,000 inhabitants was New Mexico. That year, the crime rate was ******** crimes per 100,000 people. In comparison, New Hampshire had the lowest crime rate at ****** crimes per 100,000 people. Crime rate The crime rate in the United States has generally decreased over time. There are several factors attributed to the decrease in the crime rate across the United States. An increase in the number of police officers and an increase in income are some of the reasons for a decrease in the crime rate. Unfortunately, people of color have been disproportionately affected by crime rates, as they are more likely to be arrested for a crime versus a white person. Crime rates regionally The District of Columbia had the highest rate of reported violent crimes in the United States in 2023 per 100,000 inhabitants. The most common crime clearance type in metropolitan counties in the United States in 2020 was murder and non-negligent manslaughter. The second most dangerous city in the country in 2020 was Detroit. Detroit has faced severe levels of economic and demographic declines in the past years. Not only has the population decreased, the city has filed for bankruptcy. Despite the median household income increasing, the city still struggles financially.
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Population growth drives increasing demand for housing, jobs, food, education, transportation and many services. Population decline is the flip side of that dynamic, creating its own pressures on local business, government, housing and people.This map shows which areas are under significant pressure from population growth or decline. As the population of the U.S. continues to grow, the cities and the suburbs are experiencing changes in their population density. This map shows areas of declining density in brown, and high growth in dark green.Red areas will lose population by 2015, while green areas will grow. Darker green areas will grow more than 1.25% per year. Click on the map for details about an area. Use this map as a backdrop for your organization's locations, services areas, or other subjects. There is also a simple app showing this web map.You candownload the data from this map package.