According to a survey conducted in December 2021, ** percent of clinicians in the Asia Pacific and South America regions believed that in future they will make the majority of their decisions using clinical decision support tools that use artificial intelligence (AI). On the other hand, fewer than ** percent of clinicians surveyed in Europe and North America agreed that the majority of clinical decisions in ten years' time will be based on AI.

Worldwide revenues from the artificial intelligence (AI) software market is forecast to increase from 2018 to 2025. North America is the largest regional market which also experiences the largest market growth, with revenues increasing from around *****billion U.S. dollars in 2018 to more than ***billion U.S. dollars in 2025. Asia-Pacific and Europe are the other major regional players in the global AI software. AI technologies are being used in a variety of situations across consumer, enterprise, and government markets. A greener economy with AI technology   The use of AI for environmental applications is predicted to reduce global greenhouse gas (GHG) emissions worldwide. North America and Europe are the regions with the most significant reductions in GHG, with emissions estimated to reduce by *** and *** percent respectively by 2030. The use of AI for sustainable environmental applications is also predicted to increase regional net employment and GDP. East Asia is forecast to increase its workforce by *** percent in 2030 through jobs created by AI environmental applications, the equivalent of around *****million added jobs. Europe is projected to be the region whose economy could benefit the most from using AI sustainability applications, increasing its GDP potentially by *** percent in 2030.

Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographics GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

Analysis of ‘COVID-19 Vaccination Demographics by County and District’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7ec34b28-73d7-4b56-a100-c2e3bcbcd279 on 27 January 2022.

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

Vaccination demographics data by county/region, by race, by ethnicity, by gender, and by age.

Fields with less than 5 results have been suppressed.

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

Analysis of ‘Local Area Unemployment Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/452841df-b9bd-4e65-abda-ffad4f0bc242 on 13 February 2022.

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

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort which produces monthly estimates of produces monthly and annual employment, unemployment, and labor force data for approximately 7,000 areas including Census regions and divisions, States, counties, metropolitan areas, and many cities.

This dataset includes data for all 50 states, the District of Columbia, and Puerto Rico. To only see data for Connecticut, create a filter where "State name" is equal to "Connecticut".

For more information on the LAUS program and data visit: https://www.bls.gov/lau/

For more information from the CT Department of Labor visit: https://www1.ctdol.state.ct.us/lmi/LAUS/default.asp

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

Technology professionals in Asia had the highest weekly adoption of artificial intelligence (AI) tools with a rate of **** percent in 2023. In contrast, the United States had the lowest adoption rate of AI tools with only about ** percent.

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort which produces monthly estimates of produces monthly and annual employment, unemployment, and labor force data for approximately 7,000 areas including Census regions and divisions, States, counties, metropolitan areas, and many cities. For more information and data visit: https://www.bls.gov/lau/

Analysis of ‘ESRI Population Projections by Local Authority’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-87d49471-33e5-4ec8-8cde-69ed29de6da2 on 12 January 2022.

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

Population projection by scenario, year of age and local authority, for the 4 scenarios described in the project methodology for years 2017-2040. https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level

The 4 scenarios are:

Baseline/Business as usual – based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon.

50:50 City – based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf)

High Migration – assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a)

Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter.

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

Analysis of ‘Urban Village Demographic Area Profile ACS 5-year 2006-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0c713e66-67fe-406c-b5c5-77fef49beec5 on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2006-2010

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

Analysis of ‘Population, employment and gross domestic product (regional structural data)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-www-datenportal-bmbf-de-portal-1-10-2 on 16 January 2022.

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

Table 1.10.2: Population, employment and gross domestic product (regional structural data)

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

Analysis of ‘Population by age group on 31.12. ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-region-statistik-nord-de-detail_timeline-13-1102-5-1-354-1325- on 18 January 2022.

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

Population — Population (Official Population Update) — Population by age groups on 31.12.

on the HTML offer of the time series

regional data for Schleswig-Holstein

Statistical Office for Hamburg and Schleswig-Holstein

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

Analysis of ‘An Urban Village Demographic Area Profile ACS 5-year 2013-2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/184ba13d-303d-4e77-857c-c15c929ef2f1 on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2013-2017

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

Analysis of ‘Urban Village Demographic Area Profile ACS 5-year 2009-2013’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0aaf66ea-c9a1-4da8-ae28-f04f53ddcd86 on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2009-2013

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

Analysis of ‘An Urban Village Demographic Area Profile ACS 5-year 2013-2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a35b80dd-199a-4da2-8340-2f70a13e6b17 on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2013-2017

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

Most respondents around the world had tried generative AI tools at least once in 2023, though China and Europe were significantly higher in that regard than other regions. In fact Greater China had the highest overall use, with only ** percent neither knowing or having no exposure to generative AI. Developing markets had the least interaction with generative AI overall, though they were the likeliest ones to use it just outside of work.

Geolocet's Administrative Boundaries Spatial Data serves as the gateway to visualizing geographic distributions and patterns with precision. The comprehensive dataset covers all European countries, encompassing the boundaries of each country, as well as its political and statistical divisions. Tailoring data purchases to exact needs is possible, allowing for the selection of individual levels of geography or bundling all levels for a country with a discount. The seamless integration of administrative boundaries onto digital maps transforms raw data into actionable insights.

🌐 Coverage Across European Countries

Geolocet's Administrative Boundaries Data offers coverage across all European countries, ensuring access to the most up-to-date and accurate geographic information. From national borders to the finest-grained administrative units, this data enables informed choices based on verified and official sources.

🔍 Geographic Context for Strategic Decisions

Understanding the geographical context is crucial for strategic decision-making. Geolocet's Administrative Boundaries Spatial Data empowers exploration of geo patterns, planning expansions, analysis of regional demographics, and optimization of operations with precision. Whether it is for establishing new business locations, efficient resource allocation, or policy impact analysis, this data provides the essential geographic context for success.

🌍 Integration with Geolocet’s Demographic Data

The integration of Geolocet's Administrative Boundaries Spatial Data with Geolocet's Demographic Data creates a synergy that enriches insights. The combination of administrative boundaries and demographic information offers a comprehensive understanding of regions and their unique characteristics. This integration enables tailoring of strategies, marketing campaigns, and resource allocation to specific areas with confidence.

🌍 Integration with Geolocet’s POI Data

Combining Geolocet's Administrative Boundaries Spatial Data with our POI (Points of Interest) Data unveils not only the administrative divisions but also insights into the local characteristics of these areas. Overlaying POI data on administrative boundaries reveals details about the number and types of businesses, services, and amenities within specific regions. Whether conducting market research, identifying prime locations for retail outlets, or analyzing the accessibility of essential services, this combined data empowers a holistic view of target areas.

🔍 Customized Data Solutions with DaaS

Geolocet's Data as a Service (DaaS) model offers flexibility tailored to specific needs. The transparent pricing model ensures cost-efficiency, allowing payment solely for the required data. Whether nationwide administrative boundary data or specific regional details are needed, Geolocet provides a solution to match individual objectives. Contact us today to explore how Geolocet's Administrative Boundaries Spatial Data can elevate decision-making processes and provide the essential geographic data for success.

Analysis of ‘Urban Village Demographic Area Profile ACS 5-year 2009-2013’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f61ade1c-a2ec-4394-aa92-249ac0104008 on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2009-2013

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

Artificial Intelligence (AI) in Sports Analytics Market Outlook

According to our latest research, the global Artificial Intelligence (AI) in Sports Analytics market size reached USD 2.8 billion in 2024. The market is expected to grow at a robust CAGR of 28.6% during the forecast period, reaching approximately USD 25.3 billion by 2033. This remarkable growth is being fueled by the increasing adoption of AI-driven solutions for data-driven decision-making, enhanced player performance analysis, and the rising demand for personalized fan experiences across sports organizations worldwide.

One of the primary growth factors for the AI in Sports Analytics market is the exponential increase in data generated from various sporting activities, including player statistics, match footage, and biometric data. The ability of AI algorithms to process and analyze large volumes of diverse data in real time is revolutionizing how teams and coaches approach training, strategy formulation, and in-game decisions. Advanced machine learning models are enabling sports organizations to extract actionable insights that were previously unattainable, leading to improved player performance, reduced injury risks, and optimized team management. As sports become increasingly competitive, the reliance on AI-powered analytics tools is expected to intensify, further driving market expansion.

Another significant driver is the growing emphasis on fan engagement and media innovation. Sports organizations are leveraging AI to deliver personalized content, interactive experiences, and real-time statistics to fans through digital platforms and broadcast media. AI-powered systems can analyze viewer preferences, social media interactions, and historical data to tailor content and advertisements, enhancing fan loyalty and opening new revenue streams. The integration of AI in broadcasting also enables automated highlight generation, advanced commentary, and immersive viewing experiences, which are reshaping the sports entertainment landscape and contributing to the rapid adoption of AI-based analytics solutions.

The increasing collaboration between technology providers and sports entities is further accelerating the market’s growth trajectory. Partnerships between AI software developers, sports analytics firms, and professional sports teams are resulting in the development of customized solutions tailored to specific sports and organizational needs. Investments in research and development, coupled with the proliferation of cloud computing and IoT devices, are making AI-powered analytics more accessible and cost-effective. As a result, even mid-tier and amateur sports organizations are beginning to adopt these technologies, broadening the market’s addressable base and fueling sustained growth.

From a regional perspective, North America currently dominates the AI in Sports Analytics market, accounting for the largest share in 2024, thanks to the presence of leading sports franchises, advanced technological infrastructure, and high investment in sports technology. However, Europe and the Asia Pacific regions are rapidly emerging as key growth markets, driven by increasing sports commercialization, digital transformation initiatives, and the rising popularity of sports such as football, cricket, and basketball. The Middle East & Africa and Latin America are also witnessing growing adoption, albeit at a relatively slower pace, due to increasing investments in sports infrastructure and the proliferation of digital platforms.

Component Analysis

The Component segment of the AI in Sports Analytics market is bifurcated into Software and Services. Software solutions constitute the backbone of AI-driven analytics, encompassing platforms for data collection, processing, visualization, and predictive modeling. These platforms are being widely adopted by sports teams and associations for tasks such as performance tracking, tactical analysis, and injury prevention. The demand for ad

In 2024, around ** percent of clinicians surveyed worldwide reported being at least somewhat familiar with artificial intelligence. The region of North America had the largest share of clinicians that were very familiar with AI technologies, at ** percent.

Analysis of ‘Population by age group in the world as of 31.12. ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-region-statistik-nord-de-detail_timeline-13-1102-5-1-347-729- on 18 January 2022.

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

Population — Population (Official Update) — Population by age groups in the world as at 31.12.

on the HTML offer of the time series

regional data for Schleswig-Holstein

Statistical Office for Hamburg and Schleswig-Holstein

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