About this dataset

Context

The dataset tabulates the population of Rapid City by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Rapid City across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.7% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Rapid City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Rapid City total population. 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 Rapid City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Rapid City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Rapid City. The dataset can be utilized to understand the population distribution of Rapid City by age. For example, using this dataset, we can identify the largest age group in Rapid City.

Key observations

The largest age group in Rapid City, SD was for the group of age 35 to 39 years years with a population of 5,897 (7.67%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Rapid City, SD was the 80 to 84 years years with a population of 1,396 (1.82%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Rapid City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Rapid City total population. 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 Rapid City Population by Age. You can refer the same here

In 2024, the urban population in Vietnam stood at approximately 39 million people. The six largest urban agglomerations include Hanoi, Hai Phong, Da Nang, Bien Hoa, Ho Chi Minh City, and Can Tho. On the other hand, Ben Tre, Thai Binh, and Bac Giang had the lowest rates of urbanization in the country. Urbanization in Vietnam The rapid urbanization in Vietnam results in a disproportionate population density between its urban and rural areas. For instance, in 2022, Ho Chi Minh City recorded a population density of 4,481 inhabitants per square kilometer, nearly 15 times the country's average population density in the same year. The urban population is consistently increasing due to the country’s economic reforms and infrastructure development, as well as higher living standards. For example, the monthly income per capita in urban areas is nearly half as much as that in rural areas. Nevertheless, the poverty rate in Vietnam has been consistently diminishing each year, roughly at 4.2 percent as of 2022. Urban infrastructure in Vietnam Vietnam has made significant progress in developing its urban infrastructure, especially in major cities like Hanoi and Ho Chi Minh City. The expansion of highways, seaports, and airports has enhanced domestic and international connectivity, as well as greatly contributed to the country’s logistic industry. For instance, Hanoi and Ho Chi Minh City are developing a metro system which is expected to be put into operation in 2024. The country has also invested in modern healthcare facilities, schools, and commercial centers. However, challenges such as traffic jams, limited public transportation services, and environmental pollution still require significant efforts to meet the growing demands of the Vietnamese urban population.

Population Density Estimation via Satellite Market Outlook

According to our latest research, the global Population Density Estimation via Satellite market size reached USD 2.14 billion in 2024, with a robust CAGR of 11.8% projected through 2033. By the end of the forecast period, the market is expected to achieve a value of USD 6.15 billion. This sustained growth is primarily driven by the rising demand for high-precision geospatial data to support urbanization, disaster management, and environmental monitoring initiatives across both developed and emerging economies.

One of the primary growth factors for the Population Density Estimation via Satellite market is the increasing urbanization and rapid expansion of metropolitan areas worldwide. As cities become more densely populated, urban planners and policymakers require accurate, up-to-date population distribution data to optimize infrastructure, transportation networks, and public services. Satellite-based population density estimation offers a scalable, cost-effective solution that provides comprehensive spatial coverage, overcoming the limitations of traditional census methods which are often time-consuming, expensive, and infrequent. The integration of satellite imagery with advanced analytics and artificial intelligence has further enhanced the precision and timeliness of population density assessments, making them indispensable for modern urban development strategies.

Another significant driver is the growing frequency and severity of natural disasters, such as floods, earthquakes, and wildfires, which necessitate real-time population mapping for effective disaster response and resource allocation. Governments and humanitarian organizations increasingly rely on satellite-derived population density data to identify vulnerable communities, plan evacuation routes, and deploy emergency aid efficiently. The ability to monitor population movements in near real-time has proven critical during crises, enabling authorities to make informed decisions that can save lives and minimize damage. Furthermore, advancements in satellite sensor technologies, such as high-resolution optical and radar imaging, have improved the accuracy and reliability of population estimates, fostering greater adoption across disaster management agencies globally.

The market is also propelled by the expanding applications of population density estimation in sectors such as agriculture, environmental monitoring, and defense. In agriculture, understanding population distribution helps optimize land use planning and resource allocation, particularly in regions facing food security challenges. Environmental monitoring agencies utilize population data to assess human impact on ecosystems, track urban sprawl, and design conservation strategies. Meanwhile, defense and intelligence organizations leverage satellite-based population analytics for border surveillance, threat assessment, and mission planning. This broadening spectrum of use cases is encouraging both public and private sector investments in satellite-based population density estimation solutions, further fueling market growth.

From a regional perspective, North America and Europe currently dominate the Population Density Estimation via Satellite market, owing to their advanced satellite infrastructure, robust research ecosystems, and high levels of government funding for geospatial intelligence. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, increasing investments in space technology, and rising demand for smart city solutions. Countries such as China, India, and Japan are at the forefront of leveraging satellite data for urban planning and disaster management. In contrast, regions like Latin America and the Middle East & Africa are gradually adopting satellite-based population estimation technologies, supported by international collaborations and growing awareness of the benefits of geospatial intelligence.

Technology Analysis

The technology segment of the Population Density Estimation via Satellite m

Population living close to Mass Rapid Transit stops or stations in OECD urban areas. We provide city-level average statistics of people close to transit (PNT) as well as sub-city grid-maps of population density close to transit stations.Population grids are taken from the Global Human Settlement (GHS) project. The dataset used is the 250m-grid and Mollweide projected 2015 GHS-POP, available for download at: https://ghsl.jrc.ec.europa.eu/download.php?ds=pop.GTFS files can be accessed from the OpenMobilityData project at: https://transitfeeds.com or from local transit depositories (GTFS für Deutschland accessible at https://gtfs.de/, Open platform for French public data from https://www.data.gouv.fr, Transport for London Station Locations available at https://data.london.gov.uk/dataset/tfl-station-locations).Boundaries of urban areas are taken from the OECD definition of Functional Urban Areas (FUA) as available by country at: https://www.oecd.org/cfe/regional-policy/functionalurbanareasbycountry.htm.

Greywater Recycling System Market Outlook

According to our latest research, the global greywater recycling system market size reached USD 3.12 billion in 2024, driven by increasing water scarcity and heightened environmental awareness. The market is projected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 9.19 billion by 2033. The primary growth factor for this industry is the escalating demand for sustainable water management solutions, particularly in urban and industrial settings, as communities and governments worldwide seek to address the challenges posed by dwindling freshwater resources and stricter wastewater regulations.

One of the key drivers propelling the growth of the greywater recycling system market is the increasing global emphasis on water conservation. With climate change intensifying water scarcity in several regions, both public and private sectors are aggressively investing in innovative water reuse technologies. Greywater recycling systems, which collect and treat wastewater from showers, sinks, and laundries for non-potable uses such as irrigation and toilet flushing, are gaining rapid adoption. This is further supported by government incentives, building code amendments, and public awareness campaigns promoting water reuse. The integration of these systems in green building certifications such as LEED and BREEAM is also catalyzing market expansion, as developers and property owners strive to achieve higher sustainability ratings and reduce operational costs.

Another significant growth factor is the technological advancement in treatment and filtration methods for greywater. Modern greywater recycling systems now employ sophisticated biological, mechanical, and membrane filtration technologies, which ensure higher purification efficiency and reliability. These innovations have substantially lowered maintenance requirements and improved the quality of recycled water, making greywater systems more appealing for both new construction and retrofitting in existing buildings. Furthermore, the decreasing cost of advanced system components, coupled with the rising cost of potable water, is making greywater recycling economically attractive for a wider range of end-users, including commercial complexes, industrial facilities, and institutional establishments.

Urbanization and industrialization trends, particularly in emerging markets, are also fueling the demand for greywater recycling systems. Rapid population growth in cities is placing immense pressure on municipal water supplies and wastewater infrastructure. In response, city planners and industrial operators are turning to decentralized water reuse solutions, such as greywater recycling, to augment water availability and reduce environmental impact. The market is witnessing heightened activity in regions facing chronic water shortages, such as the Middle East & Africa and parts of Asia Pacific, where governments are mandating water reuse in commercial and residential developments. This regional dynamism is expected to continue as more countries adopt stringent environmental regulations and set ambitious targets for water reuse.

From a regional perspective, Asia Pacific currently dominates the greywater recycling system market, accounting for the largest share in 2024, followed by North America and Europe. The Asia Pacific market is characterized by rapid urban development, high population density, and acute water stress, especially in countries like China, India, and Australia. North America, particularly the United States, is witnessing strong growth due to increasing adoption in commercial and institutional applications, supported by favorable state-level water reuse policies. Europe is also a significant market, driven by strict environmental regulations and a mature green building sector. Meanwhile, the Middle East & Africa region is emerging as a high-growth market, propelled by government-led initiatives to address water scarcity and promote sustainable water management practices.

System Type Analy

The rapid development of oasis desert cities adversely affects fragile ecosystems, preventing regional sustainable development. This study investigates the spatiotemporal evolution characteristics and potential quantitative relationship between oasis landscape structure (OLS) and the ecological risk index (ERI) and the trend in different development scenarios in Tiemenguan City, a typical oasis city in an arid zone in northwestern China, from 1990 to 2020. We calculated the ERI thresholds for different landscape types, classified ecological risk levels, and examined the factors influencing ecological risk. The normalized difference vegetation index (NDVI) thresholds were NDVI ≥ 30% for oases, 10% 2 to 345.3 km2 during 30 years. The transition and desert zones decreased by 49.7% and 37.9%, respectively. The ERI decreased and was strongly correlated with the OLS. The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. This study quantitatively evaluated the landscape types’ ecological risk levels and analyzed the effects of dynamic migration on the landscape type stability. This paper provides a systematic research framework for ecological risk assessment of various landscape types in oasis desert cities and a scientific basis for ecological conservation and related research.

Pairwise comparison of criteria giving a higher weighting to land use.

About this dataset

Context

The dataset tabulates the data for the Rapid City, SD population pyramid, which represents the Rapid City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Rapid City, SD, is 31.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Rapid City, SD, is 30.7.
• Total dependency ratio for Rapid City, SD is 61.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Rapid City, SD is 3.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Rapid City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Rapid City for the selected age group is shown in the following column.
• Population (Female): The female population in the Rapid City for the selected age group is shown in the following column.
• Total Population: The total population of the Rapid City for the selected age group is shown in the following column.

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 Rapid City Population by Age. You can refer the same here

Origin-Destination Analytics Market Outlook

According to our latest research, the global Origin-Destination Analytics market size reached USD 6.2 billion in 2024, demonstrating robust expansion driven by increasing urbanization and the escalating need for intelligent transportation solutions. The market is expected to grow at a CAGR of 14.7% from 2025 to 2033, reaching a forecasted value of USD 20.1 billion by 2033. This growth is primarily attributed to the widespread adoption of advanced analytics in traffic management, urban planning, and logistics optimization. The integration of real-time data sources and artificial intelligence is further propelling the market, enabling organizations across various sectors to make data-driven decisions that enhance operational efficiency and user experience.

One of the primary growth factors for the Origin-Destination Analytics market is the rapid pace of urbanization and the corresponding rise in population density within major cities worldwide. As urban centers become increasingly congested, there is a critical need for sophisticated analytics tools that can process vast amounts of mobility data, enabling city planners and transportation authorities to design more efficient infrastructure and optimize traffic flow. The proliferation of smart city initiatives, coupled with substantial investments in intelligent transportation systems, is fueling the demand for origin-destination analytics. These solutions help stakeholders understand mobility patterns, predict congestion points, and develop actionable strategies to mitigate traffic-related issues, thereby improving the quality of urban life and supporting sustainable city growth.

Another significant driver of market expansion is the evolution of logistics and supply chain management practices. In an era characterized by e-commerce growth and consumer demand for rapid deliveries, logistics companies are leveraging origin-destination analytics to streamline their operations. By analyzing the movement of goods from warehouses to end customers, businesses can identify bottlenecks, optimize delivery routes, and reduce operational costs. The integration of Internet of Things (IoT) sensors and GPS tracking has further enhanced the granularity and accuracy of origin-destination data, empowering organizations to respond proactively to disruptions and maintain high service levels. As the logistics sector continues to digitize, the reliance on advanced analytics will only intensify, opening new avenues for market growth.

Technological advancements, particularly in artificial intelligence (AI) and machine learning (ML), are also playing a pivotal role in shaping the Origin-Destination Analytics market. Modern analytics platforms are increasingly capable of processing real-time data streams from diverse sources such as mobile devices, connected vehicles, and public transportation systems. These platforms employ sophisticated algorithms to uncover hidden patterns, forecast demand, and facilitate dynamic decision-making. The growing adoption of cloud computing is further democratizing access to advanced analytics, enabling organizations of all sizes to implement scalable and cost-effective solutions. As technology continues to evolve, the market is expected to witness the emergence of even more powerful analytics tools, driving innovation across multiple application areas.

From a regional perspective, North America currently leads the Origin-Destination Analytics market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of smart city technologies, substantial investments in transportation infrastructure, and a strong presence of leading analytics vendors. Meanwhile, Asia Pacific is poised for the highest growth rate during the forecast period, driven by rapid urbanization, government-led digitalization initiatives, and increasing investments in public transportation networks. Europe maintains a steady growth trajectory, supported by stringent regulatory frameworks and a focus on sustainable urban mobility solutions. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a comparatively moderate pace, as governments and private enterprises recognize the benefits of data-driven decision-making in addressing urban mobility challenges.

Component Analysis

The Origin-Destination Analytics market is segmented by component into Sof

Parcel Locker Network Market Outlook

According to our latest research, the global parcel locker network market size reached USD 1.52 billion in 2024, reflecting robust adoption across logistics, retail, and residential sectors. The market is expected to expand at a CAGR of 11.8% between 2025 and 2033, culminating in a forecasted market value of USD 4.13 billion by 2033. This impressive growth is primarily driven by the accelerating pace of e-commerce, the rising demand for secure last-mile delivery solutions, and increasing urbanization trends worldwide.

The growth trajectory of the parcel locker network market is underpinned by the exponential rise in e-commerce transactions and the corresponding need for efficient, contactless delivery solutions. As online retail continues to gain traction, consumers are demanding more flexible and secure options for receiving their parcels. Parcel lockers address these needs by enabling 24/7 access to packages, reducing missed deliveries, and enhancing the overall customer experience. Additionally, the COVID-19 pandemic has further accelerated the adoption of contactless delivery mechanisms, positioning parcel lockers as an essential component of modern logistics infrastructure. The integration of advanced technologies, such as IoT and cloud connectivity, is also enhancing the functionality and appeal of smart parcel locker systems, further propelling market growth.

Another significant growth driver is the increasing urbanization and the shift toward smart city initiatives across developed and emerging economies. Urban centers are experiencing a surge in population density, leading to greater demand for efficient and space-saving delivery solutions. Parcel lockers are being widely deployed in residential complexes, commercial buildings, retail centers, and transportation hubs to address the challenges of last-mile delivery in congested urban environments. Moreover, governments and municipalities are actively supporting the deployment of parcel locker networks as part of broader smart city strategies, recognizing their role in reducing traffic congestion, minimizing carbon emissions, and improving urban logistics efficiency.

The market is also witnessing substantial investment from logistics providers, retailers, and technology companies seeking to enhance their delivery capabilities and gain a competitive edge. Strategic partnerships, mergers, and acquisitions are becoming increasingly common as stakeholders aim to expand their parcel locker networks and integrate value-added services such as returns management, payment processing, and real-time tracking. The rapid proliferation of modular and customizable locker solutions is enabling businesses to cater to specific customer needs and adapt to evolving market dynamics. These trends are expected to sustain the momentum of the parcel locker network market over the forecast period.

From a regional perspective, Europe currently leads the global parcel locker network market, followed closely by North America and Asia Pacific. The European market is characterized by a mature e-commerce ecosystem, high urbanization rates, and strong government support for smart logistics solutions. In North America, rising consumer expectations for convenience and the rapid expansion of omnichannel retail are fueling demand for parcel lockers. The Asia Pacific region, meanwhile, is poised for the fastest growth, driven by booming e-commerce activity, rapid urban development, and increasing investments in digital infrastructure. Latin America and the Middle East & Africa are also emerging as promising markets, supported by improving logistics networks and growing adoption of online shopping.

Component Analysis

The parcel locker network market is segmented by component into hardware, software, and services, each playing a pivotal role in the overall ecosystem. The hardware segment, which encompasses the physical locker units, electronic locks, sensors, and user interfaces, remains the backbone of the market. Manufacturers are focusing on enhancing the durability, security, and modularity of hardware components to cater to diverse installation environments, from residential complexes to outdoor public spaces. The shift toward smart parcel lockers, equipped with IoT sensors and advanced access control systems, is driving innovation within the hardware segment. Additionally, the adoption of weather-resistant materials and vandalism-proof designs is addressing the

Suitability criteria and scores.

About this dataset

Context

The dataset tabulates the Rapid City Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Rapid City, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Rapid City.

Key observations

Among the Hispanic population in Rapid City, regardless of the race, the largest group is of Mexican origin, with a population of 2,949 (67.42% of the total Hispanic population).

https://i.neilsberg.com/ch/rapid-city-sd-population-by-race-and-ethnicity.jpeg" alt="Rapid City Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Rapid City
• Population: The population of the specific origin for Hispanic or Latino population in the Rapid City is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Rapid City total Hispanic or Latino population. 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 Rapid City Population by Race & Ethnicity. You can refer the same here

Ride-Hailing Market Outlook

According to our latest research, the global ride-hailing market size reached USD 158.3 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 10.2% from 2025 to 2033, reaching a forecasted value of USD 377.5 billion by the end of 2033. This impressive growth is being driven by urbanization, increasing smartphone penetration, and the demand for convenient, cost-effective transportation solutions worldwide. As per our latest analysis, the ride-hailing market is experiencing dynamic shifts due to evolving consumer preferences, technological advancements, and regulatory developments.

The primary growth factor for the global ride-hailing market is the rapid urbanization witnessed across major cities worldwide. Urban populations are increasingly seeking efficient mobility solutions that can bypass the challenges of traffic congestion, limited parking, and environmental concerns. Ride-hailing platforms, with their on-demand, app-based convenience, have emerged as a preferred choice for daily commuters and occasional travelers alike. Furthermore, the integration of real-time GPS tracking, digital payments, and user-friendly interfaces has significantly enhanced the customer experience, leading to higher adoption rates. The proliferation of affordable smartphones and widespread internet connectivity has further accelerated this trend, making ride-hailing accessible to a broader demographic segment.

Another key driver is the ongoing innovation in vehicle types and service offerings. The ride-hailing market has diversified beyond traditional four-wheelers to include two-wheelers and three-wheelers, catering to different urban landscapes and consumer needs. Additionally, companies are expanding their portfolios to incorporate car sharing, car rentals, and station-based mobility services, thus appealing to both individual and commercial end-users. The rise of electric and hybrid vehicles within ride-hailing fleets is also supporting sustainability goals and attracting environmentally conscious consumers. These innovations are not only enhancing operational efficiency but are also positioning ride-hailing services as integral components of smart city initiatives and multimodal transport systems.

Regulatory support and strategic partnerships are also fostering the growth of the ride-hailing market. Governments in several regions are recognizing the potential of ride-hailing platforms to reduce traffic congestion, lower emissions, and improve urban mobility. As a result, regulatory frameworks are gradually evolving to accommodate new business models and technologies, including digital payment systems and autonomous vehicles. Strategic alliances between ride-hailing companies, automotive manufacturers, and technology providers are further driving market expansion by enabling the development of advanced mobility solutions and seamless user experiences. This collaborative ecosystem is expected to unlock new growth opportunities and enhance the competitiveness of the ride-hailing industry in the coming years.

From a regional perspective, Asia Pacific remains the dominant market, accounting for the largest share of global ride-hailing revenues in 2024. The region's high population density, rapid urbanization, and strong digital infrastructure have created a fertile ground for ride-hailing adoption. North America and Europe are also significant contributors, driven by high disposable incomes, advanced technology adoption, and favorable regulatory environments. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by increasing smartphone penetration and rising demand for flexible transportation options. As the market continues to evolve, regional dynamics will play a crucial role in shaping the competitive landscape and growth trajectory of the global ride-hailing market.

Service Type Analysis

The ride-hailing market is segmented by service type into e-hailing, car shari

About this dataset

Context

The dataset tabulates the population of Rapid City by race. It includes the population of Rapid City across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Rapid City across relevant racial categories.

Key observations

The percent distribution of Rapid City population by race (across all racial categories recognized by the U.S. Census Bureau): 78.83% are white, 1.40% are Black or African American, 9.29% are American Indian and Alaska Native, 1.33% are Asian, 0.09% are Native Hawaiian and other Pacific Islander, 1.20% are some other race and 7.85% are multiracial.

https://i.neilsberg.com/ch/rapid-city-sd-population-by-race.jpeg" alt="Rapid City population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Rapid City
• Population: The population of the racial category (excluding ethnicity) in the Rapid City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Rapid City total population. 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 Rapid City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Rapid City by race. It includes the distribution of the Non-Hispanic population of Rapid City across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Rapid City across relevant racial categories.

Key observations

Of the Non-Hispanic population in Rapid City, the largest racial group is White alone with a population of 59,003 (81.33% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Rapid City
• Population: The population of the racial category (for Non-Hispanic) in the Rapid City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Rapid City total Non-Hispanic population. 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 Rapid City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Rapid City. The dataset can be utilized to gain insights into gender-based income distribution within the Rapid City population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Rapid City, among individuals aged 15 years and older with income, there were 30,200 men and 28,774 women in the workforce. Among them, 15,124 men were engaged in full-time, year-round employment, while 11,288 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.01% fell within the income range of under $24,999, while 10.05% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.88% of men in full-time roles earned incomes exceeding $100,000, while 10.95% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Rapid City median household income by race. You can refer the same here