Analysis of ‘Select Demographics - ACS 2012-2016, Census 2010 - Tempe Tracts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7de89f73-2612-42cd-87e0-5be6bec9ae04 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This feature layer of Tempe's Census tracts are joined with the Census Bureau's data from the 2018 Planning Database (PDB), which was established to prepare for the upcoming 2020 Census.

--- Original source retains full ownership of the source dataset ---

The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.

This feature layer of Tempe's Census block groups are joined with the Census Bureau's data from the 2018 Planning Database (PDB), which was established to prepare for the upcoming 2020 Census. The 2018 PDB contains select operational, demographic, and socio-economic statistics from the 2010 Census and the 2012-2016 5-year ACS.For more information, see the United States Census Bureau 2018 Planning Database:https://www.census.gov/topics/research/guidance/planning-databases.html.

Analysis of ‘Internet Access - ACS 2013-2017 - Tempe Tracts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/55e52c2a-6b5c-42cc-86cf-6631c877bfb6 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Tempe Census Census Tracts and internet access by household. Data source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates, table BD28011 (Internet Subscription in Household).

--- Original source retains full ownership of the source dataset ---

The Quarterly Census of Employment and Wages (QCEW) program serves as a near census of employment and wage information. The program produces a comprehensive tabulation of employment and wage information for workers covered by Connecticut Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Data on the number of establishments, employment, and wages are reported by industry for Connecticut and for the counties, towns and Labor Market Areas (LMAs) and Workforce Investment Areas (WIAs).

The Commodity Flow Survey (CFS) is undertaken through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Research and Innovation Technology Administration, Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. This survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail and services establishments. The data from the CFS are used by public policy analysts and for transportation planning and decision making to access the demand for transportation facilities and services, energy use, and safety risk and environmental concerns. This dataset provides data for the Hazardous Materials Series.

Primary Uses:

Credit Checking and Approval Matching Industry and Customer Benchmarking and Analysis The Basis for Research Projects and Reports

Subscription Includes:

Continuous Live Updating and Bespoke Delivery Infinite selections across over 90,000 organisations Granular Organisational Categorisation across Three Levels. Sector Specific Demographics and Intelligence Identification of Publicly Funded Entities Hierarchies, Group Structures and Organisational Relationships Unique ORG ID’s with full integrity and Official ID’s for many organisations

Drone-Assisted Wildlife Population Census Market Outlook

According to our latest research, the global Drone-Assisted Wildlife Population Census market size reached USD 512.6 million in 2024, driven by the rapid adoption of advanced drone technologies across conservation and wildlife management sectors. The market is projected to grow at a robust CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 1,473.2 million by 2033. This impressive growth is fueled by increasing investments in ecological monitoring, stricter wildlife protection regulations, and the widespread integration of high-resolution imaging and AI-powered data analytics in wildlife research.

The primary growth factor for the Drone-Assisted Wildlife Population Census market is the urgent need for accurate, efficient, and minimally invasive wildlife monitoring methods. Traditional wildlife census techniques often involve manual surveys, which are time-consuming, expensive, and potentially disruptive to animal habitats. Drones, equipped with advanced imaging technologies such as thermal, multispectral, and LiDAR sensors, offer a transformative alternative. These aerial systems enable researchers and conservationists to conduct large-scale surveys over challenging terrains, collect high-resolution data, and monitor elusive or endangered species without direct human interference. As biodiversity conservation becomes a global priority, especially in the face of climate change and habitat loss, the demand for drone-assisted census solutions is expected to rise significantly.

Another significant driver is the evolution of regulatory frameworks and governmental support for wildlife conservation initiatives. Many countries are enacting policies that encourage the use of unmanned aerial vehicles (UAVs) in environmental monitoring, anti-poaching efforts, and habitat mapping. This regulatory backing not only legitimizes the use of drones in protected areas but also opens up funding opportunities for research institutes and conservation organizations. Furthermore, collaborations between government agencies, NGOs, and private technology providers are fostering innovation in drone hardware and software, making these solutions more accessible and cost-effective for end-users worldwide. The growing ecosystem of partnerships and supportive policies is a critical catalyst in expanding the market’s reach.

Technological advancements in drone platforms and imaging sensors are also reshaping the landscape of wildlife population census. The integration of AI-driven analytics, real-time data transmission, and cloud-based processing has dramatically improved the accuracy and efficiency of wildlife monitoring. Drones now offer extended flight durations, improved payload capacities, and enhanced obstacle avoidance, making them suitable for diverse ecological environments. The ability to process vast amounts of visual and thermal data using machine learning algorithms allows for automated species identification, population estimation, and behavioral analysis. These innovations not only enhance data quality but also reduce operational costs, further accelerating the adoption of drone-assisted census methods across multiple regions and applications.

From a regional perspective, North America and Europe are leading the market, supported by strong research infrastructure, proactive conservation policies, and substantial funding for environmental initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by increasing awareness of biodiversity loss, expanding protected areas, and rapid technological adoption. Latin America and the Middle East & Africa, with their rich biodiversity and expansive wildlife reserves, are also witnessing growing investments in drone-based monitoring solutions. However, regional disparities in regulatory frameworks, technological access, and funding availability continue to influence market dynamics, shaping the competitive landscape and growth opportunities across different geographies.

Drone Type Analysis

This EnviroAtlas dataset is the base layer for the Milwaukee, WI EnviroAtlas area. The block groups are from the US Census Bureau and are included/excluded based on EnviroAtlas criteria described in the procedure log. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports broken down by agricultural and nonagricultural commodities. The USDA endpoint in the Census data API provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Analysis of ‘🇺🇸 US Population: 2060 Projection by age’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-population-2060-by-agee on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The US is headed toward a shortage of people of working age, and we need more immigrants or more babies. Our rapidly growing senior population will put more stress on the federal budget and the overall economy, without a sufficient share of people of working age.

Source: Table 9 at: http://www.census.gov/population/projections/data/national/2014/summarytables.html

Acknowledgements

If you use this dataset in your research, please credit Gary Hoover

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This EnviroAtlas dataset is the base layer for the Salt Lake City, UT EnviroAtlas area. The block groups are from the US Census Bureau and are included/excluded based on EnviroAtlas criteria described in the procedure log. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Conservatism-Only and Conservatism + Demographics Models of Comfort with AI, Study 1.

Businesses, researchers, and developers often seek out web activity datasets and databases to: Understand consumer behavior. Train machine learning models. Perform market research or competitor analysis. Optimize user experience on websites. Personalize content and advertising. This data can be used for a variety of different use cases

Analysis of ‘All-Island Religion (SA)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/92c6db4a-d011-4c3f-967e-94588625fb18 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

This file contains variables from the Religion Theme that was produced by AIRO using data from the census unit at the CSO and the Northern Ireland Research and Statistics Agency (NISRA). This data was developed under the Evidence Based Planning theme of the Ireland Northern Cross Border Cooperation Observatory (INICCO-2) and CrosSPlaN-2 funded research programme.

--- Original source retains full ownership of the source dataset ---

Conservatism-Only and Conservatism + Demographics Models of Perceived Risk of AI, Study 1.

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports using the North American Industry Classification System (NAICS). The NAICS endpoint in the Census data API also provides value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

This EnviroAtlas dataset portrays the total number of historic places located within each Census Block Group (CBG). The historic places data were compiled from the National Register of Historic Places, which provides official federal lists of districts, sites, buildings, structures and objects significant to American history, architecture, archeology, engineering, and culture. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

As included in the EnviroAtlas, the community level domestic water use is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Local use data, as prepared for several cities for the Chicago Metropolitan Agency for Planning and at the county level by USGS, were used. Within the Chicago study area, the 1998-2010 average estimates ranged from 33 to 196 GPD. This dataset was produced by the U.S. EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).