In 2022, the value in terms of per unit floorage of transferred residential lands in Beijing, China amounted to ****** yuan, ahead of other cities by a wide margin. Land value in terms of per unit floorage is the average land price on every unit of construction area. It constitutes the market value of housing prices together with construction costs, related taxes and fees, profits, etc.

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

Per capita values are calculated by dividing the estimated population into total revenues per city, per fiscal year.

This web service contains the following layer: OzoneReview35Cities_with2000to2014data. Full FGDC metadata records for each layer may be found by clicking the layer name at the web service endpoint (available through the online link provided above) and viewing the layer description.

Each City agency is required by rule of the Conflicts of Interest Board to disclose all not-for-profit organizations for which the agency solicits donations. This dataset lists all such organizations reported by City agencies for official fundraising occurring during calendar year 2020.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

This bar chart displays population (people) by city using the aggregation sum in the United States. The data is about cities.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000.

This data comes from the US Census Bureau's 2015 American Community Survey.

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Acknowlegement

Foto von Andrew Neel auf Unsplash

Dataset Name: City Happiness Index

Dataset Description:

This dataset and the related codes are entirely prepared, original, and exclusive by Emirhan BULUT. The dataset includes crucial features and measurements from various cities around the world, focusing on factors that may affect the overall happiness score of each city. By analyzing these factors, we aim to gain insights into the living conditions and satisfaction of the population in urban environments.

The dataset consists of the following features:

• City: Name of the city.
• Month: The month in which the data is recorded.
• Year: The year in which the data is recorded.
• Decibel_Level: Average noise levels in decibels, indicating the auditory comfort of the citizens.
• Traffic_Density: Level of traffic density (Low, Medium, High, Very High), which might impact citizens' daily commute and stress levels.
• Green_Space_Area: Percentage of green spaces in the city, positively contributing to the mental well-being and relaxation of the inhabitants.
• Air_Quality_Index: Index measuring the quality of air, a crucial aspect affecting citizens' health and overall satisfaction.
• Happiness_Score: The average happiness score of the city (on a 1-10 scale), representing the subjective well-being of the population.
• Cost_of_Living_Index: Index measuring the cost of living in the city (relative to a reference city), which could impact the financial satisfaction of the citizens.
• Healthcare_Index: Index measuring the quality of healthcare in the city, an essential component of the population's well-being and contentment.

With these features, the dataset aims to analyze and understand the relationship between various urban factors and the happiness of a city's population. The developed Deep Q-Network model, PIYAAI_2, is designed to learn from this data to provide accurate predictions in future scenarios. Using Reinforcement Learning, the model is expected to improve its performance over time as it learns from new data and adapts to changes in the environment.

Number of City park acres per 1,000 population. Tracked by fiscal year and calculated by taking the number of park acres divided by total population and multiplied by 1000.

Intercensal and postcensal population estimates for the state, counties and cities, 1990 to present.

Since APR's eCOMBS measure #3033 - Number of City Park Acres per 1,000 Population is one of the items the department is asked about the most, the Planning and Development Division created a story page and reports the fiscal year actuals.

About this dataset

Context

The dataset tabulates the data for the Danville city, VA population pyramid, which represents the Danville city population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Danville city, VA, is 30.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Danville city, VA, is 34.6.
• Total dependency ratio for Danville city, VA is 65.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Danville city, VA is 2.9.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group for the Danville city population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Danville city for the selected age group is shown in the following column.
• Population (Female): The female population in the Danville city for the selected age group is shown in the following column.
• Total Population: The total population of the Danville city for the selected age group is shown in the following column.

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 Danville city Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Switz City. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Switz City, the median household income for the households where the householder is White increased by $11,233(33.89%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $33,142 in 2013 and $44,375 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in Switz City, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Switz City.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

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 Switz City median household income by race. You can refer the same here

Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Chesapeake City, VA was 0.54% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Chesapeake City, VA reached a record high of 14.01 in May of 2022 and a record low of 0.54 in October of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Listing Price per Square Feet Year-Over-Year in Chesapeake City, VA - last updated from the United States Federal Reserve on November of 2025.

Teenage birth rate is number of live births among females ages 15 to 19 years per 1,000 females in that age group in a year. Data are for Santa Clara County residents. The measure is summarized for total county population by mother's city of residence at the time of birth. Data are presented for pooled years combined. Source: Santa Clara County Public Health Department, 2006-2015 Birth Statistical Master File; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes, sourcesTime_period (String): Year of birth. Pooled data years are presented to meet the minimum data requirements.City_name (String): Lists the mother's city of residence.Rate per 1,000 females ages 15-19 (Numeric): Teen birth rate is number of live births to mothers ages 15 to 19 years at the time of birth per 1,000 females in that age group in a year. Rate based on birth count less than 6 in a year in the area are not presented.

This bar chart displays companies by city using the aggregation count. The data is filtered where the industry is Diversified Consumer Services. The data is about companies.

In 2023, New York was the city in the United States with the most WeWork locations. New York had almost ** more locations than Boston, which was the city second in the list.

Data pulled from the US Census Bureau for response rates by City/Place for the US decennial 2020 census.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Rapid City, SD (CBSA) (MEDLISPRIPERSQUFEE39660) from Jul 2016 to Oct 2025 about Rapid City, SD, square feet, listing, median, price, and USA.