This statistic shows the number of undergraduate students at Columbia University in fall 2020, by ethnicity. In that year, ***** undergraduate students at Columbia University identified as Hispanic or Latino.

Since 2008, Columbia University has become more selective in their admissions process. In 2023, Columbia University accepted *** percent of applicants, down from ** percent in 2008. In 2023, ****** students applied to Columbia.

Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner occupied or renter occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people. That is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area.

This statistic illustrates the number of applicants to Columbia University from 2008 to 2020. In 2020, 40,084 students applied to Columbia University, an increase from 26,179 applicants in 2008.

Columbia University Mailman School of Public Health Activity File

Lifetime childhood asthma prevalence (LCAP) percentages in Puerto Rico Health Regions (HR) are substantially higher in northeastern vs. southwestern HR. Higher average relative humidity in the northeast might promote mold and mite exposures and possibly asthma prevalence. To test this hypothesis, mold contamination, Environmental Relative Moldiness Index (ERMI) values were measured in floor dust (n = 26) and dust mite allergen concentrations in bed dust (n = 14). For this analysis, the eight HR were divided into those with LCAP > 30% (n = 3) and < 30% (n = 5). The average ERMI value was significantly greater (Wilcoxon Rank Sum, p < 0.001) in high than in low LCAP HR (14.5 vs. 9.3). The dust mite antigens Der p 1, Der f 1, and Blo t 5 were detected in 90% of bed samples, but the concentrations were not significantly different in high vs. low LCAP HR. Mold exposures might partially explain the differences in LCAP HR in Puerto Rico. This dataset is not publicly accessible because: This was a study conducted by Columbia University researchers. It can be accessed through the following means: Contact: Matthew S. Perzanowski, Ph.D. Associate Professor Department of Environmental Health Sciences Mailman School of Public Health Columbia University (212) 305-3465. Format: This study was conducted by Columbia University. There is no dataset format. This dataset is associated with the following publication: Vesper , S., H. Choi, M. Perzanowski, L. Acosta, A. Divjan, B. Bolanos-Rosero, F. Rivera-Mariani, and G. Chew. Mold populations and dust mite allergen concentrations in house dust samples from across Puerto Rico. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH. Carfax Publishing Limited, Basingstoke, UK, 26(2): 198-207, (2016).

New York University had around 27,247 international students studying there in the academic year 2023/24, making it the most popular university for international students in the United States. NYU was followed by Northeastern University with 21,023 international students and Columbia University, which hosted 20,321 international students.

This dataset is about books. It has 1 row and is filtered where the book publisher is Columbia University School Publishing. It features 2 columns including publication date.

CIESIN Columbia University is a leading research center within the Columbia Climate School at Columbia University. Founded in 1989, CIESIN conducts cutting-edge geospatially enabled research and develops interdisciplinary data and systems to advance understanding of human-environment interactions and support public policy and private decision making.

The center's research focuses on exploring the complex relationships between humans and the natural environment, with a particular emphasis on climate change, disaster resilience, and sustainable development. CIESIN scientists contribute to innovative thinking on managed retreat from the coastal zone, studying the development and characteristics of informal settlements, and developing datasets such as the Basic Demographic Characteristics data set: Number of dependents relative to the economically productive people.

In Columbia University's Class of 2028 (students beginning in the fall of 2024), ** percent of students were international students. This is compared to Harvard University, where ** percent of incoming students were international students.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.


Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):

- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019).

Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This dataset is about books. It has 1 row and is filtered where the book publisher is Graduate School of Architecture, Planning, and Preservation, Columbia University. It features 7 columns including author, publication date, language, and book publisher.

The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 2 (PLACE II) data set contains estimates of national-level aggregations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, a compendium of nearly 300 variables for 228 countries. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

Data for Good Meta. High resolution population estimates for the United States. Includes total population, men, women, women of reproductive age, elderly, youth, and children subgroups. Creative Commons Attribute International License.

To facilitate population data retrieval across scale, we segment spatial coverage into 130 equal sized tiles. GPU enabled spatial join via RapidsAI was employed to assign population information with each vector tile.

Reference: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 4 November 2022.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

The Customer Leadership Development Program (CLDP) market exhibits robust growth, projected at a Compound Annual Growth Rate (CAGR) of 11.5% from 2019 to 2033, reaching a market size of $26.2 billion in 2025. This expansion is driven by several factors. Increasing competition necessitates organizations to cultivate strong leadership skills within their customer-facing teams to enhance customer satisfaction, loyalty, and ultimately, profitability. The growing adoption of digital technologies and the increasing complexity of customer interactions further fuel the demand for advanced CLDPs. Program formats are diversifying to cater to varying learning styles and time constraints, with options ranging from intensive one-week programs to longer, more comprehensive training spanning several months. The custom training segment is expected to dominate the market due to its tailored approach that directly addresses specific organizational needs, while open enrollment programs offer a cost-effective alternative for smaller organizations. The North American market currently holds a significant share, driven by the presence of leading business schools and a strong emphasis on customer-centric strategies within its corporate sector. However, regions such as Asia-Pacific are showing rapid growth due to rising disposable incomes and a burgeoning middle class increasingly demanding higher quality products and services. The competitive landscape of the CLDP market is highly concentrated, with prestigious business schools like Harvard, Columbia, and Stanford leading the charge. These institutions leverage their established brand reputation and extensive industry networks to attract a high caliber of participants. However, the market also features a growing number of specialized training firms and corporate in-house programs, leading to increased competition and driving innovation in program design and delivery. The restraints on growth primarily involve the high cost of high-quality CLDPs, which can pose a barrier to entry for smaller businesses. Additionally, measuring the ROI of these programs can be challenging, sometimes hindering investment from organizations prioritizing tangible, short-term results. Nonetheless, the long-term benefits of improved customer relations and stronger leadership pipelines are expected to outweigh these challenges, sustaining the healthy growth trajectory of the CLDP market in the coming years.

The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).