Census and population data is information recorded about the population of a country, state, city, or other well-defined geographical areas. In a census, data is collected about the population demographics of residents living within the region, taking into account median income, population density, age, ethnicity, and various other key criteria for evaluation.
Census data is commonly drawn from sources such as:
Data scientists can use information in these surveys for data visualization and to analyze the general population of a chosen area. With this data, analysts can come to practical conclusions about a given population's social and economic status in a region. These datasets can be used as a basis to build a campaign that drives social and economic policy.
The government of a country conducts the national census of population, which occurs every ten years (i.e. the 2020, and 2010 censuses). Most countries have a department or office dedicated to censuses or national statistics, meaning there is only one source per country. Other sources of population data include public records of vital events, which are defined as births, deaths, marriages, divorces, separations, annulments, and adoptions, for example.
All the data included in a national census is usually not available to collect by private companies. However, the general public can access demographic sample surveys, population registers, and international publications regarding population (like the UN Demographic Yearbook), and secondary population data is also readily accessible from journals, newspapers, magazines, and annual research reports, and more.
Usually, a census collects data of on the population of a country, such as the U.S. population, and the demographics mentioned above based on these categorizations:
In short, census and population data attributes can be considered the segmentation of a particular population's demographics.
Methods of testing the quality of census and population data collected by the government are scarce and infrequent. Despite those setbacks, these methods are usually reliable and trustworthy enough to provide accurate information about a country's population once every ten years.
To properly assess data quality, a data scientist must possess a comprehensive understanding of the tools used to collect them. There are two ways a data analyst can evaluate private surveys and datasets:
Furthermore, a data scientist has to scrutinize the response options or phrasing used to formulate a request for an answer. The way that a questionnaire is designed and curated has an impact on the measurement quality of the responses.
As an example, "What is the combined income of your household?" may prompt a respondent to answer only income generated by the main breadwinners, but not dependents who may be running their own side income. A data scientist must carefully consider the introductions provided to both respondents and interviewers alike.
Governments are the most common end users of census and population data. Acquiring these data points is crucial to improving schooling, welfare, and other state funding allocated to a particular city or area. By making predictive models based on census data, governments can effectively overcome or prevent those potential complications within a population. For example, the population division of the United Nations can use census data in their intergovernmental processes. Nowadays, this data is also published in a wide variety of formats accessible to various industries and any interested citizen.
B2B marketing organizations have also adopted the benefits of accessing population data and economic census information for their business decisions. The level of detail that census data products capture is unrivaled, with information about neighborhood traffic, housing conditions of certain demographics, even down to the type of energy providers individual households have in service. This is invaluable for small and medium businesses as well as businesses aiming to target and nurture very specific prospects.
The difficulties of census-taking have risen exponentially due to the rising complexities of the modern world. The most common problem in taking a census is overcounting or undercounting people. Overcounting happens when a person is counted twice, and undercounting occurs when a person is not counted in a census.
Major challenges around overcounting or undercounting center around these key criteria:
Privacy concerns involve people who prefer to remain invisible, such as refugees, people who distrust the government, and people engaging in illegal activity. This reduces levels of cooperation and increases the costs involved in accurately gauging the actual population size of these groups.
Complex living arrangements can occur where one person residing in multiple locations is overcounted; for example, when a child is away at college, they can be counted both at school and at home by their parents. In contrast, those with temporary living arrangements, such as refugees, nomads, and the homeless, tend to be undercounted due to not having a permanent address.
Issues of accessibility include difficulty in accessing gated communities and unsafe areas, language barriers, low literacy rates, and lack of digital infrastructure such as poor internet or mobile reception.
Population data and demographic data are similar to census data. Many businesses use various types of demographic data and consumer behavior data to understand their prospects and customers. Consumer data categories are used for gaining audience insights and targeting.
You can find a variety of examples of consumer and demographic data in the Explorium Data Gallery.
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Census data lends itself well to both simple visual graphs and complex statistical models. These visualization and statistical tools also offer invaluable insights into small areas and small demographic groups, which sample data would be unable to capture with precision. Attributes in a census are considered very useful for tracking trends in consumer behavior.
Similar behavior and choices tend to arise among members of the same demographics both consistently and predictably, which industries in advertising, insurance, banking, eCommerce, retail, and fintech can mine for insights in:
External data such as foot traffic, social media presence, pricing, demographic data, and weather data can help improve predictive models for a variety of use cases.
Beyond government funding allocation, census data is crucial for private business development. Businesses use this information to determine where to build new stores or factories—decisions that can create jobs and improve the local economy.
The higher education sector is one of the biggest users of census and population data. Media companies require data from towns, cities, and enclaves to report on daily and seasonal movement of traffic. They require such data for research purposes related to a wide array of humanities subjects such as politics, economics and social demographics.
Banking and fintech heavily utilize census and population data. The relationship between personal attributes and individual economic attributes play an important role in determining prospects who are prime leads for loans and credit card applications. Historically, only censuses have been able to accurately gather data regarding these key attributes, which is why the finance sector relies on census and population data to formulate their business decisions.
Once you find the data providers that you want to purchase new data from, it is essential to validate their data quality, coverage, gaps, recency, frequency of updates, risks, and relevance to your particular use case. Having the right data onboarding frequency will keep your data current, accurate, and relevant.
When acquiring data from an external database, data processing and preparation includes data cleaning, data transformation, and configuring data pipelines for data consumption. If you purchase data from a data marketplace, it will likely need to be reformatted to match your internal data. Ideally, you need to be able to access the data via Excel, CSV export, or API integration, and have connection capabilities with storage tools such as AWS S3 and Snowflake. Predictive model performance (or drift) should be continuously monitored to ensure the model is still performing according to your business expectations and requirements.
Another option is to use an external data platform, like Explorium's data platform, which will automatically match and integrate data signals to your own data. An external data platform not only provides data access, but also enables every step of the process from data discovery, and integration to predictive model training and deployment. This saves your business precious time and resources.