Our People data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

People Data Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

People Data Use Cases:

360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori People Data you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of People Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic...

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.

Satellite NTN for Census Operations Market Outlook

According to our latest research, the global Satellite NTN for Census Operations market size reached USD 1.94 billion in 2024, driven by the increasing need for reliable and real-time connectivity in census data collection, especially in remote and underserved regions. The market is poised to expand at a robust CAGR of 13.7% from 2025 to 2033, with a forecasted market size of USD 6.09 billion by 2033. This growth is primarily attributed to advancements in satellite non-terrestrial networks (NTN), growing digital transformation in government operations, and the rising importance of accurate demographic data for policy-making and resource allocation.

One of the most significant growth factors for the Satellite NTN for Census Operations market is the increasing demand for comprehensive and accurate population data across both developed and developing economies. Governments worldwide are recognizing the necessity of leveraging advanced technologies to overcome traditional barriers in census operations, such as geographical inaccessibility, limited infrastructure, and logistical challenges. Satellite NTN solutions enable seamless data collection and transmission from even the most remote and hard-to-reach locations, ensuring that every demographic segment is represented. This capability is particularly vital in regions with challenging terrains, dispersed populations, or frequent natural disasters, where conventional terrestrial networks are either unreliable or non-existent. As a result, the adoption of satellite NTN for census operations is becoming a strategic priority for national statistical agencies and international organizations seeking to enhance the accuracy and timeliness of population data.

Another critical driver for the market's expansion is the rapid technological advancement in satellite communication infrastructure. The emergence of low Earth orbit (LEO) satellite constellations, improvements in data compression algorithms, and the integration of artificial intelligence for data analytics have collectively transformed the landscape of census operations. These innovations have not only reduced the latency and cost associated with satellite communications but have also enabled real-time data transmission and processing capabilities. Consequently, census operations can now be conducted more efficiently, with reduced operational risks and enhanced data security. Furthermore, the scalability and flexibility of satellite NTN solutions make them ideal for supporting large-scale, periodic census activities as well as continuous demographic monitoring initiatives. This technological evolution is anticipated to drive sustained investments in the Satellite NTN for Census Operations market over the coming years.

The market is also benefiting from increased collaboration between public and private sector stakeholders. Governments are partnering with satellite service providers, technology vendors, and research organizations to develop customized solutions tailored to specific census requirements. These partnerships are fostering innovation and enabling the deployment of integrated platforms that combine hardware, software, and services for end-to-end census management. Additionally, international development agencies and non-governmental organizations (NGOs) are leveraging satellite NTN to conduct population surveys and humanitarian assessments in crisis-affected regions. The growing emphasis on data-driven decision-making for social and economic development is further fueling demand for advanced census solutions, positioning the Satellite NTN for Census Operations market for continued growth throughout the forecast period.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The dominance of North America is attributed to its mature satellite communication infrastructure, high adoption of digital technologies in government operations, and significant investments in census modernization initiatives. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by large-scale population surveys, government digitization programs, and expanding satellite coverage in emerging economies such as India and China. Europe remains a key market due to its robust regulatory framework and emphasis on data privacy and security. Latin America and the Middle East & Africa are also experiencing steady

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

About this dataset

Context

The dataset tabulates the Columbus population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Columbus across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Columbus was 23,035, a 1.16% decrease year-by-year from 2022. Previously, in 2022, Columbus population was 23,305, a decline of 1.28% compared to a population of 23,607 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Columbus decreased by 2,728. In this period, the peak population was 25,763 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Columbus is shown in this column.
• Year on Year Change: This column displays the change in Columbus population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Columbus Population by Year. You can refer the same here

As of October 2025, India had the largest Instagram audience worldwide, with 480.55 million users. The United States ranked second with 181.75 million users, followed by Brazil with 147 million. However, Turkey recorded the highest audience reach, with 92.1 percent of its population using the platform. It took Instagram 11.2 years to reach the milestone of 2 billion monthly active users worldwide. Instagram’s demographics in the United States As of March 2025, Instagram was the fourth most visited social media service in the United States, after Facebook, Pinterest and X. Out of TikTok, Instagram and Snapchat, TikTok was the most used of all three platforms by Generation Z. Overall, 57 percent of Gen Z social media users used Instagram in 2021, down from 61 percent in 2020 and 64 percent in 2019. Instagram finds most popularity with those in the 25 to 34 year age group, and as of January 2025, roughly 28.3 of all users in the United States belonged to this age group. The social media app was also more likely to be used by women. Most followed accounts on Instagram Instagram’s official account had the most followers as of April 2024 with over 672 million followers. Manchester United forward Cristiano Ronaldo (@cristiano) had over 628 million followers on the platform, while the Argentinian footballer Lionel Messi (@leomessi) had over 502 million followers. The Instagram accounts of the American singer and actress Selena Gomez (@selenagomez) and the media personality and makeup mogul Kylie Jenner (@kyliejenner) had over 400 million followers each.

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.

As of June 2020, there were ** e-commerce websites in India who had reached more than *** percent of the country's digital population. Comparatively, just ***** e-commerce websites in Vietnam had reached more than *** percent of the digital population as of June 2020.

Food Trucks Services Market Outlook

The global food trucks services market size was valued at approximately USD 3.93 billion in 2023 and is projected to reach USD 7.72 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.8% during the forecast period. The market is growing due to a combination of urbanization, lifestyle changes, and increasing demand for fast, convenient food options. As urban areas expand and consumer preferences shift towards more personalized and unique dining experiences, food trucks have emerged as a dynamic and versatile food service solution.

One of the primary growth drivers for the food trucks services market is the low initial investment and operational costs compared to traditional brick-and-mortar restaurants. Entrepreneurs can enter the food industry with a relatively modest upfront investment, which is highly appealing in an economic environment where startup capital can be challenging to secure. Additionally, the flexibility to move to different locations offers food truck operators the ability to reach diverse customer bases and maximize revenue potential. This adaptability has been particularly advantageous during times of economic uncertainty, as it allows operators to respond swiftly to changing market conditions.

Another significant factor contributing to the growth of the food trucks services market is the increasing consumer demand for gourmet and specialty foods. Food trucks have gained popularity for their ability to offer unique and high-quality cuisine that is often absent from fast-food chains and traditional restaurants. The trend towards healthier eating and the availability of diverse international cuisines have further fueled this demand. Food truck operators often emphasize fresh, locally-sourced ingredients, which resonates well with the growing number of health-conscious consumers.

Technological advancements have also played a crucial role in the expansion of the food trucks services market. Innovations in kitchen equipment, payment systems, and digital marketing have enabled food truck operators to streamline their operations and enhance customer engagement. Mobile ordering apps and social media platforms allow customers to locate food trucks, view menus, place orders, and leave reviews, thereby creating a seamless and interactive dining experience. This tech-savvy approach has not only attracted a younger demographic but has also fostered customer loyalty and repeat business.

Mobile Food Vending Trailers have emerged as an innovative solution within the food trucks services market, offering a unique blend of mobility and functionality. These trailers provide an alternative for entrepreneurs looking to enter the food industry with even lower initial investment compared to traditional food trucks. Equipped with essential kitchen facilities, mobile food vending trailers can be easily attached to vehicles, allowing operators to access locations that might be challenging for larger food trucks. This flexibility is particularly advantageous in urban areas with limited parking or in regions where food truck regulations are more stringent. By offering a compact and efficient setup, mobile food vending trailers cater to the growing demand for diverse and convenient food options, while also enabling operators to experiment with different cuisines and menu offerings.

Regionally, North America dominates the food trucks services market, driven by a well-established culture of street food and high consumer spending on dining out. The United States, in particular, has a vibrant food truck scene, with cities like Los Angeles, New York, and Austin serving as major hubs. However, the market is also experiencing significant growth in other regions, such as Asia Pacific and Europe, where urbanization and changing consumer preferences are driving demand. Countries like China and India are witnessing a surge in food truck operations, supported by government initiatives to promote small businesses and street food culture.

Type Analysis

The food trucks services market is segmented into mobile food trucks and stationary food trucks. Mobile food trucks, which are the most common type, offer the advantage of mobility, allowing operators to change locations to reach different customer demographics and attend various events and festivals. This flexibility is particularly beneficial in densely populated urban areas where foot traffic can vary significantly by location and time. Mobile food tr

Context

In demographics, the world population is the total number of humans currently living, and was estimated to have reached 7,800,000,000 people as of March 2020. It took over 2 million years of human history for the world's population to reach 1 billion, and only 200 years more to reach 7 billion. The world population has experienced continuous growth following the Great Famine of 1315–1317 and the end of the Black Death in 1350, when it was near 370 million. The highest global population growth rates, with increases of over 1.8% per year, occurred between 1955 and 1975 – peaking to 2.1% between 1965 and 1970.[7] The growth rate declined to 1.2% between 2010 and 2015 and is projected to decline further in the course of the 21st century. However, the global population is still increasing[8] and is projected to reach about 10 billion in 2050 and more than 11 billion in 2100.

Content

Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. Annual population growth rate. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Statistical Concept and Methodology

Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time. The growth rate is computed using the exponential growth formula: r = ln(pn/p0)/n, where r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods. For information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).

Acknowledgements

Derived from total population. Population source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision, ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Television Location Tourism Market Outlook

As per our latest research, the global television location tourism market size reached USD 1.92 billion in 2024, demonstrating a robust expansion fueled by the increasing popularity of film and television-inspired travel. The market is anticipated to grow at a CAGR of 12.7% from 2025 to 2033, reaching an estimated USD 5.68 billion by 2033. This dynamic growth is primarily driven by the rising influence of global media, the proliferation of streaming platforms, and the growing consumer desire for immersive, experiential travel that connects them with their favorite shows and movies.

One of the primary growth factors propelling the television location tourism market is the unprecedented surge in content consumption across streaming platforms. With the advent of globally accessible content through services like Netflix, Disney+, and Amazon Prime, audiences are increasingly drawn to the real-world locations showcased in their favorite series and films. This has led to a significant rise in travel interest, where fans seek to relive iconic scenes and immerse themselves in the narratives that captivated them on screen. Moreover, the integration of social media and digital marketing has amplified awareness, making it easier for potential tourists to discover, plan, and book unique television-themed travel experiences. This synergy between entertainment and tourism has not only expanded the market’s reach but has also diversified its offerings to cater to a broader demographic spectrum.

Another crucial growth driver is the strategic collaboration between tourism boards, production companies, and travel agencies. Recognizing the economic potential of television-induced travel, local governments and tourism authorities are increasingly partnering with studios to promote filming locations as must-visit destinations. These collaborations often include guided tours, themed events, and exclusive behind-the-scenes experiences, which significantly enhance the value proposition for travelers. Additionally, the rising trend of experiential and adventure tourism, particularly among millennials and Gen Z, has further accelerated demand for television location tourism. These younger demographics are seeking more than just sightseeing; they desire authentic, story-driven experiences that allow them to engage with the cultural and historical context of the locations they visit.

Technological advancements have also played a pivotal role in shaping the television location tourism market. The proliferation of virtual reality (VR) and augmented reality (AR) experiences has enabled fans to explore television locations remotely, thereby expanding the market beyond physical travel. While virtual tours do not entirely replace the allure of on-site visits, they serve as effective marketing tools, inspiring future travel and providing accessibility to a global audience. Furthermore, the development of user-friendly booking platforms and mobile applications has streamlined the customer journey, making it easier for travelers to customize and manage their television-themed itineraries. These innovations, combined with the growing emphasis on personalized travel experiences, are expected to sustain the market’s momentum in the coming years.

From a regional perspective, North America and Europe currently dominate the television location tourism market, owing to their rich cinematic history and the presence of iconic filming destinations. However, the Asia Pacific region is rapidly emerging as a lucrative market, driven by the global popularity of K-dramas, Bollywood, and other regional productions. Countries like South Korea, India, and Japan are witnessing a surge in international visitors eager to explore the settings of their favorite shows. Meanwhile, Latin America and the Middle East & Africa are gradually gaining traction, supported by government initiatives and the growing international appeal of their local content. This regional diversification is expected to further fuel the market’s growth, as more destinations leverage their unique cultural assets to attract television-inspired travelers.

Very Important

• Check out the new must-see kernel for this dataset Click Here
• Make Sure to upvote for more datasets and kernel :D

Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

Walking Cane Market Outlook

According to our latest research, the global walking cane market size reached USD 1.43 billion in 2024. The market is expected to grow at a steady CAGR of 4.7% during the forecast period, with projections indicating the market will reach USD 2.18 billion by 2033. This growth is primarily driven by the rapidly aging global population, increasing prevalence of mobility-related disorders, and growing awareness regarding assistive mobility devices. The walking cane market is experiencing significant expansion as demand surges not only from the geriatric demographic but also from adults recovering from injuries or surgeries.

One of the primary growth factors fueling the walking cane market is the demographic shift toward an aging population worldwide. As per the United Nations, the number of people aged 60 years and above is expected to double by 2050, reaching over 2 billion. This demographic trend directly correlates with a higher incidence of mobility impairments, arthritis, osteoporosis, and other musculoskeletal disorders, which in turn fuels the demand for walking canes. Furthermore, the growing emphasis on active aging and independent living among seniors has led to an increased adoption of walking aids, as these devices offer enhanced mobility, balance, and fall prevention. The integration of advanced ergonomics and lightweight materials has also made modern walking canes more appealing and user-friendly, further contributing to market growth.

Another significant growth driver is the rising prevalence of chronic conditions and injuries requiring temporary or permanent mobility support. Conditions such as stroke, Parkinson’s disease, and post-surgical rehabilitation often necessitate the use of walking canes for improved stability and independence. Healthcare professionals are increasingly recommending walking canes as part of comprehensive rehabilitation programs, which has boosted their adoption across various age groups. Additionally, technological advancements in walking cane design, such as the incorporation of smart sensors, adjustable height features, and enhanced grip ergonomics, have broadened the appeal of these products beyond traditional users, catering to a wider spectrum of mobility needs.

The walking cane market is also benefiting from the expansion of distribution channels and increased accessibility. The proliferation of online stores and e-commerce platforms has made it easier for consumers to research, compare, and purchase walking canes from the comfort of their homes. This shift has been particularly significant in regions with limited access to physical medical supply stores. Moreover, growing awareness campaigns by healthcare organizations and manufacturers about the benefits of mobility aids have played a crucial role in destigmatizing the use of walking canes, encouraging more individuals to seek assistance when needed. The market is further bolstered by supportive government programs and insurance coverage for assistive devices in several countries, making walking canes more affordable and accessible to those in need.

From a regional perspective, North America continues to dominate the global walking cane market, accounting for the largest share in 2024. This is attributed to the region’s advanced healthcare infrastructure, higher healthcare expenditure, and a significant elderly population. Europe follows closely, with strong demand driven by similar demographic trends and supportive healthcare policies. Meanwhile, the Asia Pacific region is witnessing the fastest growth rate, propelled by a rapidly aging population, increasing healthcare awareness, and improving access to assistive devices in emerging economies such as China, Japan, and India. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as awareness and accessibility continue to improve in these regions.

Product Type Analysis

The walking cane market is segmented by product type into folding canes, quad canes, offset canes, standard canes, and others. Folding canes have gained significant traction in recent years due to their portability and convenience, especially among urban dwellers and travelers. These canes can be easily folded and stored in bags or luggage, making them ideal for users who are frequently on the move. The demand for folding canes is further bolstered by advancements in lightweight materials such as aluminum and

About this dataset

Context

The dataset tabulates the Columbus population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Columbus across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Columbus was 3,786, a 1.20% increase year-by-year from 2022. Previously, in 2022, Columbus population was 3,741, an increase of 0.97% compared to a population of 3,705 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Columbus decreased by 601. In this period, the peak population was 4,404 in the year 2003. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Columbus is shown in this column.
• Year on Year Change: This column displays the change in Columbus population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Columbus Population by Year. You can refer the same here

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

About this dataset

Context

The dataset tabulates the Columbus Grove population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Columbus Grove across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Columbus Grove was 2,131, a 0.37% decrease year-by-year from 2022. Previously, in 2022, Columbus Grove population was 2,139, a decline of 0.23% compared to a population of 2,144 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Columbus Grove decreased by 57. In this period, the peak population was 2,188 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Columbus Grove is shown in this column.
• Year on Year Change: This column displays the change in Columbus Grove population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Columbus Grove Population by Year. You can refer the same here

This web map is part of SDGs Today. Please see sdgstoday.orgGlobally, an estimated 58% of students will not reach minimum proficiency levels (MPL) in reading and mathematics by the time they finish primary school. For Sub-Saharan Africa, 88% percent of students will not reach those same MPLs. SDG 4 aims to ensure inclusive and equitable quality education for all, but access remains a major challenge. While enrollment rates continue to increase, other quality barriers remain for many students and learners. Physical distance to educational facilities is one such barrier.Using open-source georeferenced data and satellite data products, we construct travel-time isochrones from school locations and overlay subnational population counts to construct a dataset of age-specific population counts within travel-time catchment areas in Africa. The resulting walk-time data can help support gaps in existing education data and highlight open-source methods. Our School location data is derived from OpenStreetMap, a powerful open-source data repository of georeferenced buildings, roads, amenities, and other physical features. While OpenStreetMap is vast, data quality varies by region and many schools remain missing across the globe. Please review the methodological note where we discuss the implications of missing data. Finally, population data is derived from WorldPop constrained Sex/Age demographic population images.Additional school locations are georeferenced using ArcGIS 123 Survey data collected as a part of My School Today!, an SDGs Today call to action that encourages participants to georeference school buildings in Africa with OpenStreetMap.For more information, contact SDGs Today at sdgstoday@unsdsn.org.

Tropical mountains are global hotspots for birdlife. However, there is a dearth of baseline avifaunal data along eleva-tional gradients, particularly in Africa, limiting our ability to observe and assess changes over time in tropical montane avian communities. In this study, we undertook a multi-year assessment of understory birds along a 1,750 m elevational gradient (1,430-3,186 m) in an Afrotropical moist evergreen montane forest within Ethiopia's Bale Mountains. Analyzing 6 years of systematic bird-banding data from 5 sites, we describe the patterns of species richness, abundance, community composition, and demographic rates over space and time. We found bimodal patterns in observed and estimated species richness across the elevational gradient (peaking at 1,430 and 2,388 m), although no sites reached asymptotic species richness throughout the study. Species turnover was high across the gradient, though forested sites at mid-elevations resembled each other in species composition. We found significant variation across sites in bird abundance in some of the dietary and habitat guilds. However, we did not find any significant trends in species richness or guild abundances over time. For the majority of analyzed species, capture rates did not change over time and there were no changes in species' mean elevations. Population growth rates, recruitment rates, and apparent survival rates averaged 1.02, 0.52, and 0.51 respectively, and there were no elevational patterns in demographic rates. This study establishes a multi-year baseline for Afrotropical birds along an elevational gradient in an under-studied international biodiversity hotspot. These data will be critical in assessing the long-term responses of tropical montane birdlife to climate change and habitat degradation.

Methods Statistical Analyses

Community-level Analyses

To test whether our survey effort had adequately surveyed the local bird community, we calculated rarified species accumulation curves across sampling days for each site, based on observed and expected (sample-based rarefaction) species richness (Colwell et al. 2012) using the “exact” method of the specaccum function from the R package VEGAN (Oksanen et al. 2019). Since our species accumulation curves did not reach asymptotes for species richness, observed species richness likely does not capture true species richness. We, therefore, used sample-size-based rarefaction and extrapolation (R/E) of Hill numbers (the effective number of species, which integrates species richness and relative abundances; Chao et al. 2014). Sample-size-based rarefaction and extrapolation of Hill numbers is an emerging approach used to standardize and compare estimates of diversity between samples (see Cox et al. 2017, Fair et al. 2018, Baumel et al. 2018, Chao et al. 2019, Debela et al. 2020). Specifically, we used this framework to estimate two values of Hill number 0 (i.e. estimated species richness). First, we calculated standardized species richness. We used the function iNEXT from the R package iNEXT (Hsieh et al. 2016) to calculate R/E curves, standardizing our curve parameters to a maximum of 1,000 individual bird captures (endpoint = 1,000), knots = 500, and a bootstrap replication of 1,000 (nboot = 1,000). From these curves, we provide standardized estimates of species richness based on the sampling of 1,000 individuals at each site. We also estimated asymptotic species richness using the function ChaoRichness from the package iNEXT (Hsieh et al. 2016). Although the asymptotic species richness is an estimate of true species richness, in practice, reaching an asymptote can take a long time and a lot of sampling. We then plotted the R/E curves of standardized species richness (i.e. over 1,000 individuals) for each site as a function of sample size using the function ggiNEXT (Hsieh et al. 2016). We also visualized asymptotic species richness by setting the endpoint of the iNEXT function to 10,000 individuals.

Next, we assessed the spatial and temporal patterns in observed species richness and guild-specific captures. For guild-specific captures, we identified the primary diet and habitat association of each species using a global dataset of avian ecological traits (Table 1; see Şekercioğlu et al. 2004, 2019 for a description of the dataset) and summed captures for each separate guild based on either primary diet or habitat. We restricted our analyses to guilds that had ≥40 captures and ≥5 species over the study period and modeled each guild independently. We chose a ≥40 capture threshold as our cutoff between infrequently and frequently encountered species. Most species above this threshold were recorded each year and more than once or twice in each year (the few species that were not recorded each year were recorded multiple times in the other years), whereas individuals under this threshold tended to have few captures across more than one year. We chose a ≥5 species threshold for the guild models to ensure that results for these metrics represented more than a few species.

We constructed models comparing each response variable (observed species richness, dietary, and habitat guild-specific captures) as a function of the site, and included the number of survey days per site and year (Table 2) as a covariate to control for the variation in the sampling effort. We used generalized linear models (GLMs) for species richness and guild-specific captures, as these represent count data. Within the GLMs, we used a Poisson error structure for species richness, and for guild-specific captures, we used a quasi-Poisson error structure to account for over-dispersion in the count data. To assess changes in the bird community over time, we ran an additional model for each response variable that contained year and site, with a year * site interaction (error structures were applied as above). We tested the significance of the explanatory variables in the GLMs with an analysis of deviance.

We assessed species dissimilarity between sites along the elevational gradient by calculating the Sørenson dissimilarity index (S8) for pairs of sites adjacent to each other along the elevational gradient, as well as for Chiri-1430 and Dinsho-3186 at either end of the gradient. S8 can range from complete dissimilarity (S8 = 1) to complete similarity (S8 = 0). This dissimilarity can be further decomposed into turnover and nestedness, which we calculated using the function beta.pair in the package betapart (Baselga et al. 2020). Finally, to compare community composition (captures of different species, weighted by abundance), we ran a Principal Coordinate Analysis (PCoA) based on a Bray-Curtis dissimilarity matrix (Legendre and Legendre 2012). A PCoA extracts the greatest orthogonal axes of variation in community composition, plotting them in multidimensional space such that more similar communities are closer to each other in Euclidean space. We extracted the first two axes from the PCoA that represent the greatest variation in community composition.

Species-level Analyses

As a proxy for species abundance (Dulle et al. 2016), we calculated species-specific captures (the number of captured and recaptured individuals of a particular species) per site and year for the most frequently-captured species (≥ 40 captures over the study period). To assess the variation in species’ elevational distributions, we calculated the mean elevation at which each species was detected each year (hereafter “mean elevation”) for frequently-captured species that were detected at least once in every year of the study. Smaller range shifts in tropical birds are more detectable when analyzing mean elevational occurrence rather than the changes in upper or lower range boundaries, as the position of range boundaries is strongly dependent on the sampling effort (Shoo et al. 2006).

We regressed both species-specific captures (in a GLM with a quasi-Poisson error structure) and mean elevation (in a simple linear model) against year. Since the Dinsho-3186 site was located far from the other sites, we decided to re-run the species-level analyses with Dinsho-3186 data removed. The results remained similar with Dinsho-3186 excluded (Supplemental Material Tables S2 and S3) and, therefore, we retained Dinsho-3186 data in the analyses to increase our statistical power. Additionally, we compared our elevational records for banded birds with those reported in the literature for Ethiopia and the Horn of Africa (Ash and Atkins 2009, Dowsett and Dowsett-Lemaire 2015, Rannestad 2016) in order to assess whether any species were detected outside of their recorded elevational distributions. We used an elevational difference of at least 150 m to indicate whether a species had clearly been recorded in our study higher or lower than previously reported in Ethiopia, a distance previously used to signify extralimital records of birds in Ethiopia (Dowsett and Dowsett-Lemaire 2015). A difference of <150 m could result from chance, whereas a difference >150 m is more likely to result from a systematic change in the elevational range.

At the population level, we used Pradel models (Pradel 1996) implemented with the package RMark (Laake and Rexstad 2012) to estimate the rates of apparent survival (φ), recruitment (F), and realized population growth (λ) while controlling for encounter probabilities (p). φ is the rate at which individuals remain in the population; F is the rate at which new individuals join the population via birth or immigration; and λ is the combined effect of survival and recruitment. A population does not change in size when λ = 1, declines when λ <1, and grows when λ >1. These mark-recapture models cannot distinguish movement in and out of a study area (immigration/emigration) from true birth and survival. However, birds living in tropical mountains are known to have small range sizes (Orme et al. 2006), and tropical

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