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License information was derived automatically
All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name
More than 250 million tweets in Spanish from 331 Spanish-speaking cities in Latin America, Spain and the United States were compiled from Twitter. In this data set, a column is provided with the 5000 most frequent words and one with their corresponding frequencies (the number of times the word was produced in that city) for each of the 331 cities. The reported data correspond to the years 2009 to 2016.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Spanish Fork household income by gender. The dataset can be utilized to understand the gender-based income distribution of Spanish Fork income.
The dataset will have the following datasets when applicable
Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).
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/.
Explore our comprehensive data analysis and visual representations for a deeper understanding of Spanish Fork income distribution by gender. 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
The database is one of the results of the project "Metropolitan Governance in Spain: Institutionalization and Models" (METROGOV, 2020-23), funded by the National R&D Plan 2019 of the Ministry of Science and Innovation (PID2019-106931GA-I00). Directed by Professor Tomàs, the project wants to understand the building and definition of models of metropolitan governance in Spain. There is no comprehensive work based on a common methodology that address this topic, this is why the METROGOV project seeks to cover this gap in the literature and the research.
The first specific goal of the project was to create a database of metropolitan institutions in Spain, including hard forms like metropolitan governments, metropolitan sectorial agencies and consortiums as well as soft forms as metropolitan strategic plans. The database provides an updated and rigorous portrait of the institutional thickness of urban agglomerations, gathering up to 384 metropolitan cooperation instruments in the Spanish functional areas. In other words, it is a picture of the institutional reality of Spanish urban agglomerations. This database provides precious information about the model of metropolitan governance, the municipalities involved and the sectors with most and less institutionalization.
As in Spain there is not an official or statistical definition of metropolitan areas, the project departed from the concept of Functional Urban Areas (FUA), considered as “densely inhabited city and a less densely populated commuting zone whose labour market is highly integrated with the city” (Eurostat). According to this definition, the commuting zone contains the surrounding travel-to-work areas of a city where at least 15 % of employed residents are working in a city. In the case of Spain, we find 45 big FUA, where the central city has more than 100.000 inhabitants. The database was structured considering these 45 Spanish FUAs, and it was necessary that at least 3 municipalities participated in the metropolitan cooperation tools.
In the grid, you will find the 384 instruments of metropolitan cooperation following different criteria. First of all, the models of metropolitan governance, from hard to soft: metropolitan government, metropolitan sectoral agency, “mancomunidad”, consortium, public or public-private company, territorial plan, sectoral plan, comarca, association of municipalities, strategic plan, European project, working group. Each instrument is also classified according to the subject of cooperation: transport, waste, water, housing, urbanism, etc. Other complementary information is added, such as: the year of creation; number and names of municipalities that are part of the entity; percentage of territory covered by this tool, etc.
A book has been recently published with the results of the project: Tomàs, M. (2023) (ed.). Metrópolis sin gobierno. La anomalía española en Europa. València: Tirant lo Blanch.
With 6.2 Million Businesses in Spain , Techsalerator has access to the highest B2B count of Data/Business Data in the country.
Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...
Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.
At Techsalerator, we cover all regions and cities in Spain with Business Data.
A few example of regions in Spain : Andalusia Catalonia Community of Madrid Valencian Community Galicia Castile and León Basque Country Castilla-La Mancha Canary Islands Region of Murcia Aragon Extremadura Balearic Islands Asturias Navarre Cantabria La Rioja
A few examples of cities in Spain : Madrid Barcelona Valencia Sevilla Zaragoza Malaga Murcia Palma Las Palmas de Gran Canaria Bilbao Alicante Cordoba Valladolid Vigo Gijon Eixample L'Hospitalet de Llobregat Latina Carabanchel A Coruna Puente de Vallecas Sant Marti Gasteiz / Vitoria Granada Elche Ciudad Lineal Oviedo Santa Cruz de Tenerife Fuencarral-El Pardo Badalona Cartagena Terrassa Jerez de la Frontera Sabadell Mostoles Alcala de Henares Pamplona Fuenlabrada Almeria Leganes San Sebastian Sants-Montjuic Santander Castello de la Plana Burgos Albacete Horta-Guinardo Alcorcon Getafe Nou Barris Hortaleza San Blas-Canillejas Salamanca Tetuan de las Victorias Logrono La Laguna City Center Huelva Arganzuela Badajoz Sarria-Sant Gervasi Sant Andreu Salamanca Chamberi Usera Tarragona Chamartin Lleida Marbella Leon Villaverde Cadiz Retiro Dos Hermanas Mataro Gracia Santa Coloma de Gramenet Torrejon de Ardoz Jaen Moncloa-Aravaca Algeciras Parla Delicias Ourense Alcobendas Reus Moratalaz Ciutat Vella Torrevieja Telde Barakaldo Lugo San Fernando Girona Santiago de Compostela Caceres Lorca Coslada Talavera de la Reina El Puerto de Santa Maria Cornella de Llobregat Las Rozas de Madrid Orihuela Aviles El Ejido Guadalajara Roquetas de Mar Palencia Algorta Pozuelo de Alarcon Sant Boi de Llobregat Toledo Les Corts Pontevedra Getxo Gandia Sant Cugat del Valles Ceuta Arona Torrent Chiclana de la Frontera Manresa San Sebastian de los Reyes Ferrol Velez-Malaga Ciudad Real Mijas Melilla
https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal
Table of INEBase Population employed aged 16 and over by sex, place of birth (Spain/abroad), occupation 1D (Provincial capitals and main cities). Annual. Municipalities. Censo de Población
https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal
Table of INEBase Relationship with the activity: Population aged 16 and over by sex, age groups, place of birth (Spain/foreign) and relationship with the activity (grouped) (Provincial capitals and main cities). Annual. National. Censo de Población
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Spanish Fork. 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 2011 and 2021, 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
https://i.neilsberg.com/ch/spanish-fork-ut-median-household-income-by-race-trends.jpeg" alt="Spanish Fork, UT median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Racial categories include:
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 Spanish Fork median household income by race. 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
Avg Housing Price: Free Market: Barcelona data was reported at 3,300.100 EUR/sq m in Sep 2018. This records an increase from the previous number of 3,297.300 EUR/sq m for Jun 2018. Avg Housing Price: Free Market: Barcelona data is updated quarterly, averaging 3,071.100 EUR/sq m from Mar 2005 (Median) to Sep 2018, with 55 observations. The data reached an all-time high of 3,950.200 EUR/sq m in Jun 2008 and a record low of 2,385.200 EUR/sq m in Mar 2014. Avg Housing Price: Free Market: Barcelona data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.
🇪🇸 스페인
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Description of the Dataset of Properties in Spain
This dataset contains 542 records of properties available in Spain, with a total of 19 columns that describe different aspects of each property, from its location and price to specific characteristics such as surface, number of rooms, and bathrooms, among others. The columns and their main characteristics are detailed below:
1. Energy_Consumption: Energy consumption in kWh/m² per year. Has missing values.
2. Reference: Unique reference code for each property, numeric type.
3. Heating: Heating type. Has missing values.
4. Country: The country where the property is located, is always "es" (Spain).
5. City: City of the property.
6. Zone: Specific zone within the city, 492 valid records.
7. Energy_Class: Energy class of the property. Has missing values.
8. Publish_date: Date and time the property was published in `YYYY-MM-DD HH:MM:SS` format.
9. Sale_Price: Sale price in Euros.
10. Floor: The floor on which the property is located. Has missing values.
11. Street: Address of the property, with 507 unique streets.
12. Bedrooms: Number of bedrooms, presented as text (e.g., `3 bedr.`).
13. Elevator: Indicates whether the property has an elevator (only `Yes` or null).
14. Bathrooms: Number of bathrooms, with values like `1 bath` and `2 baths`.
15. Year_Construction: The year the property was built.
16. Surface: The surface area of the property in square meters.
17. Autonomous_Community: Autonomous Community of the property.
18. Contrat: Type of contract (`sale` or `rent`).
19. Property_Type: Type of property.
This dataset is suitable for the analysis of property characteristics in the Spanish real estate market, whether to identify price trends, surface distribution, energy efficiency, or to assess the popularity of certain areas and types of properties.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Spanish Fort. 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 2011 and 2021, 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
https://i.neilsberg.com/ch/spanish-fort-al-median-household-income-by-race-trends.jpeg" alt="Spanish Fort, AL median household income trends across races (2011-2021, in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Racial categories include:
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 Spanish Fort median household income by race. 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
Several recent European studies conducted over the past 50 years have documented a positive connection between a person’s height and their salary. However, there are very few studies for earlier periods and for southern Europe. In this paper, we analyze the relationship between the height of conscripts born between 1888 and 1907 and their daily wages in 1924. Data for the Spanish city of Zaragoza was used. The results showed that for every additional 10 cm of height, an individual earned approximately 3% more. Furthermore, the shortest 25% of individuals suffered a considerable penalty in their income (about 15%). To understand the causes of this discrimination, we then analyzed the data by socioeconomic group. We found that people in low socioeco nomic groups essentially suffered wage discrimination. This finding could be linked to the fact that a tall stature conveys an image of strength and productivity. It should be noted that these results were found mainly for the urban areas, with their relatively large labor supply and weak blood ties rather than rural areas or among immigrants. In other words, the height penalty affected the weakest groups of society (low socioeconomic level and immigrants).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Avg Housing Price: Free Market: More than 5 Years Old: Alicante data was reported at 1,122.200 EUR/sq m in Mar 2018. This records an increase from the previous number of 1,101.400 EUR/sq m for Dec 2017. Avg Housing Price: Free Market: More than 5 Years Old: Alicante data is updated quarterly, averaging 1,080.400 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 1,461.800 EUR/sq m in Jun 2010 and a record low of 1,035.000 EUR/sq m in Mar 2014. Avg Housing Price: Free Market: More than 5 Years Old: Alicante data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Avg Housing Price: Free Market: More than 5 Years Old data was reported at 1,559.400 EUR/sq m in Mar 2018. This records an increase from the previous number of 1,550.700 EUR/sq m for Dec 2017. Avg Housing Price: Free Market: More than 5 Years Old data is updated quarterly, averaging 1,517.500 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 1,835.500 EUR/sq m in Mar 2010 and a record low of 1,445.100 EUR/sq m in Sep 2014. Avg Housing Price: Free Market: More than 5 Years Old data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data was reported at 1,155.800 EUR/sq m in Mar 2018. This records an increase from the previous number of 1,138.600 EUR/sq m for Mar 2016. Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data is updated quarterly, averaging 1,385.700 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 19 observations. The data reached an all-time high of 1,617.400 EUR/sq m in Jun 2010 and a record low of 1,097.500 EUR/sq m in Dec 2013. Avg Housing Price: Free Market: Less than 5 Years Old: Badajoz data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data was reported at 951.800 EUR/sq m in Mar 2018. This records a decrease from the previous number of 953.500 EUR/sq m for Dec 2017. Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data is updated quarterly, averaging 1,041.900 EUR/sq m from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 1,730.100 EUR/sq m in Dec 2011 and a record low of 947.400 EUR/sq m in Sep 2014. Avg Housing Price: Free Market: More than 5 Years Old: Ciudad Real data remains active status in CEIC and is reported by Ministry of Public Works. The data is categorized under Global Database’s Spain – Table ES.P003: Housing Prices: Free Market: by Region and Major City.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name