Data from several censuses and surveys are available for download from this site. Using the Advanced Search option presented on the homepage, users can easily begin searching for data for their geographic area. The US Census Bureau recommends starting by selecting Geographies to narrow down the area of interest. From there, users can either search by Topic, Race & Ethnic Groups, Industry Codes or EEO Occupation Code. Once a dataset has been selected, users are presented with a variety of options, such as Modify Table, Add/Remove Geographies, Bookmark/Save, Print, Download and Create a Map. Data can be downloaded as a shapefile, PDF, Excel Spreadsheet or Rich Text Format (.rtf).

The Census Program Data Viewer (CPDV) is an advanced web-based data visualization tool that helps make statistical information more interpretable by presenting key indicators in a statistical dashboard. It also enables users to easily compare indicator values and identify relationships between indicators.

Students will explore U.S. census data to see the spatial differences in the United States’ population. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:· Unit 2, A1: Geographical analysis of population (density, distribute and scale)· Unit 2, A3: Geographical analysis of population (composition: age, sex, income, education and ethnicity)· Unit 2, A4: Geographical analysis of population (patterns of fertility, mortality and health)Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

Employment Service (ES) is one component of the suite of services known as Employment Ontario (EO). ES provides Ontarians with access to all the employment services and supports they need in one location, so they can find and keep a job, apply for training, and plan a career that’s right for them. The goal of the ES program is to help Ontarians find sustainable employment.

Employment Service is delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services provided by ES are tailored to meet the individual needs of each client and can be provided one-on-one or in a group setting.

Employment Service has two broad categories: unassisted and assisted services.Unassisted services, or the Resource and Information (RI) service component, provides individuals with information on local training and employment opportunities, community service supports, and resources to support independent or “unassisted” job search. These services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. The RI component also helps employers to attract and recruit employees and skilled labour by posting positions and offering opportunities to participate in job fairs and other community events.

This service component is available to all Ontarians as there are no eligibility or access requirements.

Assisted services are offered to individuals who display the need for more intensive, structured, and/or one-on-one employment supports, and includes the following components: job search assistance (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interests with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)

Each assisted services client has a service plan, which is developed with the assistance of the service provider. This service plan lists all of the ES components that the client accesses, and the service provider monitors, evaluates, and adjusts this plan over the duration of the service plan. When an assisted services client completes the ES components of his/her service plan, the service provider closes the service plan (i.e. exit). As closed service plans cannot be reopened, if the client subsequently returns to access the assisted services of Employment Service (either at the same service delivery site or a different service delivery site), a new service plan is created.

To be eligible for assisted services, clients must be unemployed (defined as working less than an average of twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.Definitions for fields in this layer are available in the abbreviated Technical Dictionary.

Amazonia has been under considerable development pressure as croplands and pasture are established in areas formerly occupied by tropical forest and cerrado. Although this region is an important part of several important planetary biogeochemical cycles, the location and impact of human land use are not well understood. In particular, there is no existing satellite-based map of agriculture across the Amazon or Tocantins river drainage basins. Recent efforts have classified land cover across this vast region, although they disagree on the location and amount of cropland and do not directly address pasture, a land use that has grown in importance in the last 2 decades. Here we present an analysis of land cover and land use practices over the Amazon and Tocantins basins of South America. In this study, we demonstrate how satellite imagery and agricultural censuses can be merged in order to provide a geographically explicit, fine- scale description of land cover and land use practices. The result depicts the fraction of each 5-min (9 x 9 km) grid cell that was devoted to agricultural activity during the mid-1990s. The resultant map retains many of the characteristics of the agricultural census data, but with a much finer spatial resolution. During the mid-1990s, cultivated area is estimated to have been 1.7 x 10(7) ha (2.5% of the basin), natural pasture is estimated at 3.3 x 10(7) ha (4.9% of the basin), and planted pasture is estimated to cover 3.3 x 10(7) ha (4.9% of the basin). Perhaps more important than the quantities, however, is that these data sets provide a new blend of ground- based and satellite-based spatially explicit data. This snapshot can be used as a basis to project either forward or backward in time, as a new check of finer scale land use classifications or as a driver of ecosystem models.

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections: Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)

Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50-ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-yr intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m2 seedling plot in the center of every 5 x 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings)...

The data included in this dataset are bird and mammal observations from aboard research cruises. There are six tables of data: a transect log and observation log pairing for each of the three types of cruises. California Cooperative Oceanic Fisheries Investigations (CalCOFI) cruises are conducted quarterly off the coast of southern and central California. National Marine Fisheries (NMFS) cruises are a part of the Rockfish Recruitment Survey off the coast of southern and central California. Data were also collected aboard North Pacific Continuous Plankton Recorder (NPCPR) cruises from June 2002 through May 2006. Observations of birds and mammals include both number and behavior in addition to temporal and spatial information recorded along the cruise transect. Data are collected in order to research the interdependent aspects of the marine environment, including the effects of natural and human based climate change, and the broad implications and influences of ocean currents, weather patterns, fishing practices and coastal development on marine food webs and ecosystem processes.

Species and behavior definitions for data codes are available in the "Supplemental Documents" section.

Data are of numbers of breeding pairs of Adelie penguins(Pygoscelis adeliae)in the Ross Sea region colonies counted from aerial photographs. Photographs were taken from either a helicopter or from a C-130 Hercules. Photos were printed, populations counted, catalogued and filed. Since 2004, digital photography was used and in 2011 semi-automatic penguin counting software was developed to speed up the counting and validation process. Ross Island colonies were censused annually, colonies along the Victoria Land coast were censused as logistics allowed. Data collection started in 1981 in response to a proposed krill fishery and is ongoing. The aim was to locate all Adelie penguin colonies in the Ross Sea Region, and to count the numbers breeding at each from aerial photographs. The work attempts to relate annual changes in numbers of penguins breeding and their breeding success to weather, sea ice and other climate parameters in order to distinguish between responses due to natural events and those induced by commercial exploitation (such as fishing) or human disasters. A total of 39 colonies were found in the Ross Sea. The work is ongoing.

This dataset contains information of individual trees in Tucunare, Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree. Methodology:Tree information was obtained by leveraging the High-Resolution 1 m Global Canopy Height map generated by Meta's AI model, trained on a large dataset of satellite images and LIDAR data, to predict canopy heights with a mean absolute error of 2.8 meters. The map tiles were downloaded from Amazon Web Services (AWS) S3 Bucket and processed using additional steps: merging and clipping tiles to cover the region of interest of Tucunare, vectorizing, filtering, and georeferencing individual trees to derive attributes like area, perimeter, equivalent diameter, and circularity representing the visual crown of each tree, and finally applying zonal statistics to assign tree height values. The resulting vector layer contains detailed tree metrics, supporting advanced canopy analysis.

Success.ai’s Consumer Marketing Data API empowers your marketing, analytics, and product teams with on-demand access to a vast and continuously updated dataset of consumer insights. Covering detailed demographics, behavioral patterns, and purchasing histories, this API enables you to go beyond generic outreach and craft tailored campaigns that truly resonate with your target audiences.

With AI-validated accuracy and support for precise filtering, the Consumer Marketing Data API ensures you’re always equipped with the most relevant data. Backed by our Best Price Guarantee, this solution is essential for refining your strategies, improving conversion rates, and driving sustainable growth in today’s competitive consumer landscape.

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   • Access premium-quality consumer marketing data at unmatched prices, ensuring maximum ROI for your outreach efforts.

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In 2023, full-time workers in their 40s had the highest average annual salary in the United Kingdom, earning on average around 39,491 British pounds a year, compared with 20,437 for people aged between 18 and 21.

This dataset contains information of individual trees in Hacienda San Jose (HSJ), Vichada-Colombia extracted from remote sensing imagery processed using AI models. Information from the High Resolution 1-meter Global Canopy Heights map from Meta-AI. The dataset includes a CSV file containing attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree. Methodology:Tree information was obtained by leveraging the High-Resolution 1 m Global Canopy Height map generated by Meta's AI model, trained on a large dataset of satellite images and LIDAR data, to predict canopy heights with a mean absolute error of 2.8 meters. The map tiles were downloaded from Amazon Web Services (AWS) S3 Bucket and processed using additional steps: merging and clipping tiles to cover the region of interest of Hacienda San Jose, vectorizing, filtering, and georeferencing individual trees to derive attributes like area, perimeter, equivalent diameter, and circularity representing the visual crown of each tree, and finally applying zonal statistics to assign tree height values. The resulting vector layer contains detailed tree metrics, supporting advanced canopy analysis.

Prior to 1829, the area of modern day Greece was largely under the control of the Ottoman Empire. In 1821, the Greeks declared their independence from the Ottomans, and achieved it within 8 years through the Greek War of Independence. The Independent Kingdom of Greece was established in 1829 and made up the southern half of present-day, mainland Greece, along with some Mediterranean islands. Over the next century, Greece's borders would expand and readjust drastically, through a number of conflicts and diplomatic agreements; therefore the population of Greece within those political borders** was much lower than the population in what would be today's borders. As there were large communities of ethnic Greeks living in neighboring countries during this time, particularly in Turkey, and the data presented here does not show the full extent of the First World War, Spanish Flu Pandemic and Greko-Turkish War on these Greek populations. While it is difficult to separate the fatalities from each of these events, it is estimated that between 500,000 and 900,000 ethnic Greeks died at the hands of the Ottomans between the years 1914 and 1923, and approximately 150,000 died due to the 1918 flu pandemic. These years also saw the exchange of up to one million Orthodox Christians from Turkey to Greece, and several hundred thousand Muslims from Greece to Turkey; this exchange is one reason why Greece's total population did not change drastically, despite the genocide, displacement and demographic upheaval of the 1910s and 1920s. Greece in WWII A new Hellenic Republic was established in 1924, which saw a decade of peace and modernization in Greece, however this was short lived. The Greek monarchy was reintroduced in 1935, and the prime minister, Ioannis Metaxas, headed a totalitarian government that remained in place until the Second World War. Metaxas tried to maintain Greek neutrality as the war began, however Italy's invasion of the Balkans made this impossible, and the Italian army tried invading Greece via Albania in 1940. The outnumbered and lesser-equipped Greek forces were able to hold off the Italian invasion and then push them backwards into Albania, marking the first Allied victory in the war. Following a series of Italian failures, Greece was eventually overrun when Hitler launched a German and Bulgarian invasion in April 1941, taking Athens within three weeks. Germany's involvement in Greece meant that Hitler's planned invasion of the Soviet Union was delayed, and Hitler cited this as the reason for it's failure (although most historians disagree with this). Over the course of the war approximately eight to eleven percent of the Greek population died due to fighting, extermination, starvation and disease; including over eighty percent of Greece's Jewish population in the Holocaust. Following the liberation of Greece in 1944, the country was then plunged into a civil war (the first major conflict of the Cold War), which lasted until 1949, and saw the British and American-supported government fight with Greek communists for control of the country. The government eventually defeated the Soviet-supported communist forces, and established American influence in the Aegean and Balkans throughout the Cold War. Post-war Greece From the 1950s until the 1970s, the Marshall Plan, industrialization and an emerging Tourism sector helped the Greek economy to boom, with one of the strongest growth rates in the world. Apart from the military coup, which ruled from 1967 to 1974, Greece remained relatively peaceful, prosperous and stable throughout the second half of the twentieth century. The population reached 11.2 million in the early 2000s, before going into decline for the past fifteen years. This decline came about due to a negative net migration rate and slowing birth rate, ultimately facilitated by the global financial crisis of 2007 and 2008; many Greeks left the country in search of work elsewhere, and the economic troubles have impacted the financial incentives that were previously available for families with many children. While the financial crisis was a global event, Greece was arguably the hardest-hit nation during the crisis, and suffered the longest recession of any advanced economy. The financial crisis has had a consequential impact on the Greek population, which has dropped by 800,000 in 15 years, and the average age has increased significantly, as thousands of young people migrate in search of employment.

Matchmaking Services Market Outlook

According to our latest research, the global matchmaking services market size reached USD 12.3 billion in 2024, reflecting a strong momentum driven by digital transformation and changing social dynamics. The market is expected to grow at a CAGR of 7.1% during the forecast period, reaching a projected value of USD 22.9 billion by 2033. This robust growth is primarily fueled by rising internet penetration, the proliferation of smartphones, and a growing acceptance of online and hybrid matchmaking platforms across diverse demographics.

The most significant growth factor for the matchmaking services market is the rapid advancement of digital technology and its integration into personal and social spheres. The widespread adoption of smartphones has enabled users to access matchmaking platforms anytime and anywhere, making the process more convenient and accessible than ever before. Additionally, the implementation of artificial intelligence and data analytics in matchmaking algorithms has improved compatibility matching, increasing user satisfaction and trust in these services. The shift towards online and hybrid models has also enabled service providers to scale their offerings globally, catering to a broader audience and personalizing experiences based on user preferences and behaviors. This technological evolution is expected to remain a primary driver for market expansion over the next decade.

Another key growth factor is the shifting societal attitudes toward matchmaking and online dating services. The stigma once associated with seeking partners through digital platforms has diminished significantly, particularly among younger generations and urban populations. This change is further supported by increasing urbanization, rising disposable incomes, and growing professional commitments, which leave individuals with less time for traditional matchmaking methods. As a result, both individuals and families are increasingly turning to professional matchmaking services to find compatible partners efficiently. The market is also witnessing an uptick in demand for niche services catering to specific religious, ethnic, or lifestyle preferences, indicating a trend toward greater personalization and inclusivity in matchmaking offerings.

Furthermore, the matchmaking services market is experiencing growth due to the globalization of relationships and the increasing mobility of populations. As people migrate for education, employment, or other opportunities, the need for platforms that can bridge cultural and geographic gaps becomes more pronounced. Matchmaking services are leveraging this trend by offering features that accommodate cross-border relationships, multilingual support, and culturally sensitive matching algorithms. The rise of hybrid matchmaking, which combines the benefits of online convenience with offline personal interaction, is also gaining traction, especially in regions where traditional values still play a significant role in partner selection. These factors collectively contribute to the sustained growth and diversification of the matchmaking services market.

From a regional perspective, Asia Pacific dominates the matchmaking services market, accounting for the largest share due to its vast population, cultural emphasis on marriage, and rapid digitalization. North America and Europe follow, driven by high internet penetration and evolving social norms. The Middle East & Africa and Latin America are emerging as growth markets, supported by increasing urbanization and the gradual acceptance of online matchmaking. Each region presents unique opportunities and challenges, shaped by local cultural, religious, and socio-economic factors that influence user preferences and service adoption.

Service Type Analysis

The matchmaking services market is segmented by service type into online matchmaking, offline matchmaking, and hybrid matchmaking. Online matchmaking services have emerged as the dominant segment, driven by the convenience, accessibility, and scalability they offer. The proliferation of smartphones and widespread internet connectivity have made it easier for individuals to access these platforms from anywhere, at any time. Online matchmaking platforms leverage advanced algorithms, AI, and data analytics to provide personalized matches, enhancing user experience and satisfaction. The integration of features such as video profiles, insta