Location-based Ambient Intelligence Market Outlook

The global market size of Location-based Ambient Intelligence was valued at approximately USD 15 billion in 2023 and is projected to reach around USD 42 billion by 2032, growing at an impressive CAGR of 12.3% during the forecast period. This growth is primarily driven by the increasing integration of artificial intelligence and the Internet of Things (IoT) in various sectors, along with advancements in sensor technology and data analytics capabilities. The demand for personalized and context-aware services is another critical factor contributing to the market's expansion.

The primary growth factor for the Location-based Ambient Intelligence market is the surge in smart city initiatives worldwide, which are increasingly incorporating ambient intelligence technologies to enhance urban living conditions. These initiatives leverage data collected from various sensors and devices to optimize city operations and services. For instance, traffic management systems, energy consumption monitoring, and public safety enhancements are some applications that significantly benefit from ambient intelligence. The growing population in urban areas further necessitates the adoption of such technologies to ensure efficient resource management and improved quality of life.

Another major driver is the burgeoning adoption of smart homes, where ambient intelligence is utilized to create highly responsive and personalized living environments. Smart home devices such as thermostats, lighting systems, and security cameras, equipped with ambient intelligence, can adapt to the residents' habits and preferences. This not only enhances convenience but also contributes to energy savings and heightened security. The increasing consumer desire for technologically advanced and interconnected home environments is thus propelling the market forward.

The healthcare sector is also experiencing a transformative impact from location-based ambient intelligence. With the growing need for patient-centric care, healthcare providers are increasingly implementing ambient intelligence to monitor patients' vital signs, track their movements, and predict potential health issues before they become critical. This proactive approach enables better healthcare outcomes and reduces the burden on medical professionals. Additionally, the integration of ambient intelligence in medical facilities enhances operational efficiency and patient experience, further driving market growth.

Pervasive Computing Technology is increasingly becoming a cornerstone in the development and implementation of location-based ambient intelligence systems. This technology facilitates seamless integration and interaction between devices and their environments, enabling systems to operate more intuitively and efficiently. By embedding computing capabilities into everyday objects and environments, pervasive computing allows for continuous data collection and analysis, which is crucial for providing personalized and context-aware services. The ability to process vast amounts of data in real-time enhances decision-making processes and improves the overall functionality of ambient intelligence systems. As the demand for smart and interconnected environments grows, the role of pervasive computing technology becomes even more significant, driving innovation and expanding the possibilities for ambient intelligence applications across various sectors.

Regionally, North America is poised to dominate the Location-based Ambient Intelligence market due to its advanced technological infrastructure, high adoption rates of IoT devices, and significant investments in smart city projects. Europe follows closely, driven by strong government support and a focus on sustainable development. The Asia Pacific region is expected to witness the highest growth rate, owing to rapid urbanization, increasing investments in smart city projects, and the growing middle-class population with rising disposable incomes. Latin America and the Middle East & Africa are also showing considerable potential as emerging markets due to their increasing focus on technological advancements and infrastructure development.

Component Analysis

The Location-based Ambient Intelligence market is segmented into Software, Hardware, and Services, each playing a crucial role in the overall functioning of the system. The Software segment includes various applications and platforms that enable the integrati

Detailed store polygon data for household appliances, furniture, electronics, and related stores in the US and Canada. Essential for retail sector analysis, market strategies, and competitive intelligence in the home goods industry.

With Versium REACH's Contact Append or Contact Append Plus you can add consumer contact data, including multiple phone numbers or mobile-only to your list of customers or prospects. With Versium REACH you are connected to our proprietary database of over 300+ million consumers, 1 Billion emails, and over 150 million households in the United States. Through either our API or platform you can have contact data appended to your records with any of the following supplied values; Email Address Phone Postal Address, City, State, ZIP First Name, Last Name, City, State First Name, Last Name, ZIP

The Location-based Ambient Intelligence Market is a rapidly growing sector, with a projected market size of 19942.01 million USD by 2033. This growth is primarily attributed to the increasing demand for enhanced user experiences in various industries. The integration of artificial intelligence and the Internet of Things (IoT) has revolutionized the way we interact with our surroundings, creating a surge in demand for location-based ambient intelligence solutions. Key market drivers include the increasing adoption of smart home devices, the proliferation of IoT sensors, and the growing demand for personalized and contextualized services. The market is further segmented into various components, technologies, and end-users, with each segment experiencing unique growth trajectories. Major players in the market include Infosys Limited, Huawei Technologies Co., Ltd., SAMSUNG, ABB, Honeywell International Inc., and Schneider Electric, among others. The market is expected to witness significant growth in regions such as North America, Europe, and Asia Pacific, driven by the increasing investments in smart city initiatives and the growing awareness of the benefits of location-based ambient intelligence solutions. Recent developments include: January 2022: Lockheed Martin, Amazon, and Cisco teamed up to integrate human-machine interaction technologies into NASA's Orion ship. This provided researchers with the opportunity to learn how far-field speech technology, artificial intelligence, and tablet-based video communication can benefit astronauts in the future.. Notable trends are: Increasing social media usage is driving the market growth.

Product Overview

Homeowner Association (HOA) Data is notoriously difficult to obtain from a single source – until now. With HOA Contact Data, you can start identifying properties that are part of an association and may have restrictions for improvements. Knowing whether your prospects are a part of an HOA mitigates risk and streamlines your decision-making. You can avoid the homes that are unlikely to respond or confidently adjust your approach to be more appealing and directed toward the correct audience.

What is HOA Data?

As of 2019, more than 351,000 HOA communities exist across the United State. To be sure you’re connecting with those homeowners in a way they will respond to, it’s important to know the demographics and to have accurate, current, data powering your outreach. Our robust database covers all 50 states (encompassing 2,748 counties) and accounts for more than 49 million unique properties, providing an in-depth snapshot of HOAs across the country.

HOA Data Details

• Property Location
• HOA Name
• HOA Fee Type and Value
• HOA Fee Frequency
• HOA Contact
• HOA Contact Type
• Contact Information (Name, Address, Photo, and Email)

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Aurora:GeoStudio® is a leading geospatial analysis platform that excels in supporting Geographic Data, offering detailed and accurate information associated with specific locations on the Earth’s surface. Geographic data includes various attributes like coordinates, addresses, and place names, which can be visualized and analyzed through maps, charts, or databases. This data is crucial for understanding spatial patterns, relationships, and trends, enabling informed decision-making across various fields.

Core Features:

  1.    Comprehensive Mapping:
  •    Aurora:GeoStudio® provides access to a wide range of customizable maps, including variants from Google, Esri, Open, and Stamen. These maps offer diverse perspectives and detailed geographic information, supporting various analytical needs.
  2.    Satellite Imagery:
  •    The platform integrates high-resolution satellite imagery, allowing users to analyze land use, environmental changes, and urban development. Satellite data provides a bird’s-eye view of specific areas, enhancing the understanding of geographical contexts.
  3.    GPS Coordinates:
  •    Aurora:GeoStudio® supports precise GPS coordinates, enabling accurate location tracking and geospatial analysis. This feature is essential for applications requiring exact positioning, such as navigation, transportation planning, and asset management.
  4.    Geospatial Datasets:
  •    The platform offers extensive geospatial datasets that include information on demographics, land use, environmental conditions, and more. These datasets can be overlaid on maps to provide a richer context for analysis and decision-making.
  5.    Area Usage and Visit Analytics:
  •    Aurora:GeoStudio® provides detailed Area Usage and Visit analytics, helping users understand how different areas are utilized and frequented. This data is vital for urban planning, infrastructure management, and optimizing public spaces.
  6.    Home or Bed-Down Location Data:
  •    The platform can identify home or bed-down locations based on activity patterns, aiding in demographic analysis and urban planning.
  7.    Historic Playback:
  •    Aurora:GeoStudio® offers Historic Playback of geographic data, allowing users to review changes and trends over time. This feature is crucial for assessing the impact of past events and planning future developments.

Applications:

  1.    Urban Planning:
  •    Geographic data supports urban planners in designing more efficient and sustainable cities. By analyzing spatial patterns and relationships, planners can optimize land use, transportation networks, and public services.
  2.    Environmental Management:
  •    Geographic data is essential for monitoring and managing environmental conditions. It helps in tracking changes in land use, assessing the impact of natural disasters, and planning conservation efforts.
  3.    Transportation:
  •    Transportation planners use geographic data to optimize routes, improve infrastructure, and enhance public transit systems. Accurate geographic data ensures efficient and effective transportation planning.
  4.    Disaster Management:
  •    Geographic data plays a critical role in disaster management by providing information on vulnerable areas, assessing damage, and coordinating response efforts. It helps in planning evacuation routes and resource allocation.
  5.    Navigation:
  •    Geographic data is fundamental for navigation systems, providing accurate maps, routes, and location-based services. It enhances the user experience by offering precise and reliable navigation information.

Aurora:GeoStudio® provides robust support for Geographic Data, making it an indispensable tool for urban planning, environmental management, transportation, and more. By integrating comprehensive mapping, satellite imagery, GPS coordinates, and geospatial datasets, the platform offers valuable insights into spatial patterns, relationships, and trends. Features like Area Usage and Visit analytics, Home or Bed-Down Location data, and Historic Playback further enhance its analytical capabilities. Aurora:GeoStudio® empowers users to make informed decisions, optimize operations, and develop strategic initiatives based on accurate and detailed geographic data. This comprehensive understanding of geographic dynamics leads to better planning, resource management, and overall efficiency in various fields.

This layer contains data from the World Database of Protected Areas (WDPA) dataset for all countries within the focus of WWHGD's Combatting Wildlife Trafficking (CWT) data call (Point layer)

Resident of a smart home, who may be an old person or an Alzheimer patient needing permanent assistance, actuates the world by realizing activities, which are observed through the embedded sensors of smart home. Typically, this person may sometimes forget completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In order to provide automatic assistance for the smart home resident through the embedded electronically controllable actuators and make the smart home resident able to live independently at home we propose to calculate a possibilistic logical space for correct realization of activities, which may be represented in form of a multivariable problem. Regardless from the physical entity (modality and location) of the intelligence source and the quantity of individuals who perform the activities; per each possible goal or activity, we consider a unique source of intelligence (for example a social mind) who directs the order of fuzzy events that occur in the ambient environment, then the plan behind world actuations is modeled applying extensions of the fuzzy logic. The main key point that we deal with is the analysis of the observations in order to make inferences about possible simultaneous activities that may be planned and realized by one or more individuals; so that we can reason in the cases the parallel activities are interrupted.

Detailed store polygon data for household appliances, furniture, electronics, and related stores in the US and Canada. Essential for retail sector analysis, market strategies, and competitive intelligence in the home goods industry.

The Middle East Location-Based Services Market is Segmented by Component (Hardware, Software and Services), Location (Indoor and Outdoor), Application (Mapping and Navigation, Business Intelligence and Analytics, Location-Based Advertising, and Social Networking and Entertainment), End-User (Transportation and Logistics, IT and Telecom, Healthcare, Government, BFSI, Hospitality, and Manufacturing). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Global Online On-Demand Home Services Market size was evaluated at $5 billion in 2023 and is slated to hit $21 billion by the end of 2032, a CAGR of nearly 13%.

Geospatial data on real estate prices and housing in the USA is a strategic asset for informed decision-making in the property market. This data - at census block level - reveals regional price and construction trends, allowing real estate professionals and investors to optimize their strategies.

What is included?

At the Census Block level, this dataset includes some of the following key features:

Buildings

• Number Of Bedrooms: 0, 1, 2, 3, 4, 5+
• Year Of Construction: 1939 or before, 1940-1949...... 2010-2013, 2014 or later
• Home Occupation: Vacant, Occupied

Price and Rental Data

• Monthly Mortgage: Under $200, $200-$499...... $3000-$3999, $4000 or over
• Monthly Rent: Under $100, $100-$249...... $1500-$2499, $2500 or over
• Real-Estate Value: Under $25K, $25K-$50K...... $500K-$1M, Over $1M
• Home Ownership: Rented, Owned

Vehicles

• Number Of Vehicles In Household: 1, 2...... 4, 5+

• • Buildings: Marketers can use the number of bedrooms to target specific household sizes, tailoring campaigns for families with more members or individuals seeking different accommodations. The year of construction provides insights into the age and potential condition of homes, guiding marketers in promoting renovation or modernization services. The home occupation variable allows for targeted messaging based on the status of the property, such as addressing the needs of vacant properties versus occupied ones.
  • Price and Rental Data: These data variables provide crucial insights for marketers in the real estate and financial sectors. For example, businesses offering mortgage services can tailor their promotions based on the financial capacity of potential clients, while real estate agencies may focus on specific price ranges or ownership statuses to match their property listings with the preferences of their target audience.
  • Vehicles: Marketers can use the number of vehicles in a household to target automotive-related products and services. For instance, companies offering auto insurance or vehicle accessories may customize their campaigns based on the number of vehicles in a household, tailoring their offerings to the specific needs of potential customers.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

Global Location-Based Entertainment Market size was worth around USD 3.5 Billion in 2022 and is predicted to grow to around USD 22.2 Billion by 2030 a CAGR of roughly 26%.

Convert websites into useful data Fully managed enterprise-grade web scraping service Many of the world's largest companies trust ScrapeHero to transform billions of web pages into actionable data. Our Data as a Service provides high-quality structured data to improve business outcomes and enable intelligent decision making

Join 8000+ other customers that rely on ScrapeHero

Large Scale Web Crawling for Price and Product Monitoring - eCommerce, Grocery, Home improvement, Shipping, Inventory, Realtime, Advertising, Sponsored Content - ANYTHING you see on ANY website.

Amazon, Walmart, Target, Home Depot, Lowes, Publix, Safeway, Albertsons, DoorDash, Grubhub, Yelp, Zillow, Trulia, Realtor, Twitter, McDonalds, Starbucks, Permits, Indeed, Glassdoor, Best Buy, Wayfair - any website.

Travel, Airline and Hotel Data Real Estate and Housing Data Brand Monitoring Human Capital Management Alternative Data Location Intelligence Training Data for Artificial Intelligence and Machine Learning Realtime and Custom APIs Distribution Channel Monitoring Sales Leads - Data Enrichment Job Monitoring Business Intelligence and so many more use cases

We provide data to almost EVERY industry and some of the BIGGEST GLOBAL COMPANIES

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Farming for the futureItem Type: Web Mapping Application Storymap URLSummary: How science, data, and location intelligence support sustainable U.S. agriculture. Nine crops make up the majority of the U.S. agricultural market: corn, soybeans, wheat, cotton, sorghum, oats, rice, sunflower, and barley.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: Credit is given in the story or to USDA and Esri's ArcGIS StoryMaps and Living Atlas teams. Source ULR is https://nmcdc.maps.arcgis.com/home/item.html?id=795fa0328cb2497a8f960b5f3e8d2c7aFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=652115363d70456eae4a4ed337a4a86dUID: 26Data Requested: Ag CensusMethod of Acquisition: Esri Living AtlasDate Acquired: 6/16/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

The global crowd analytics market is expanding rapidly, with a market size of 7.44 billion in 2025 and a projected CAGR of 20.09% from 2025 to 2033. This growth is attributed to increasing investments in crowd management solutions by governments and businesses, growing urbanization, and heightened security concerns. The rising prevalence of smart cities and the adoption of advanced technologies, such as AI, cloud computing, and IoT, are further propelling market growth. The market is segmented based on deployment (cloud, on-premise, hybrid), application (market forecasting, revenue forecasting, incident analytics, customer management), and verticals (transportation, healthcare, retail, BFSI, media & entertainment). Geographically, North America and Europe dominate the market, with Asia-Pacific emerging as a significant growth region due to rapid urbanization and investments in smart city infrastructure. Prominent companies in the market include Spigit Inc., Crowd Dynamics, NEC Corporation, and CrowdANALYTIX Inc. Recent developments include: April 2021: CrowdVision is acquired by Skyfii to accelerate computer vision capabilities. Skyfii's current airport deployments will be doubled, and new significant international airports such as O'Hare, Heathrow, and JFK will be added. The CrowdVision technology complements Skyfii's growing list of intelligent data sources, allowing it to build the best solutions for customers based on their needs and situations., January 2022: Raydiant, the leading in-location experience management platform, announced today the acquisition of Sightcorp, a University of Amsterdam spinoff company that provides cutting-edge, proprietary AI-powered audience intelligence for digital signage, digital out-of-home (DOOH) media, and in-store analytics. Raydiant and Sightcorp will collaborate to provide brands with personalized on-screen, in-location experiences that generate anonymized audience and performance statistics., Crowd Strike, a pioneer in cloud-delivered endpoint, workload, identity, and data security, today unveiled updates to its Security and IT Operations product line for 2022. This includes a new module (Falcon Discover for IoT) to give organisations ground-breaking visibility for IoT and OT environments, as well as new capabilities for the Falcon Discover (Security Hygiene) module to aid IT and security leaders in comprehensively understanding and minimising an organization's attack surface to lower the risk of a potential breach., The REV7 OS series scanning sensor, powered by Ouster, Inc.'s next-generation L3 chip, has been introduced. Ouster, Inc. is a leading supplier of high-resolution digital lidar sensors for the automotive, industrial, robotics, and smart infrastructure industries. The all-new OSDome sensor is included in REV7, together with revised OS0, OS1, and OS2 sensors, which provide a twofold increase in range, improved object detection, higher precision and accuracy, and increased dependability., A fully managed software as a service (SaaS) version of Cloudera's data platform, which the company believes is more open than competitors in the crowded industry, will be available in 2022. Analytics and data exploration on a single platform are what Cloudera offers with the Cloudera Data Platform (CDP) One solution, which is initially only available on AWS..

The Sidewalks and Gaps feature class shows the location of sidewalks on roads that are active on the State Highway System (SHS). Sidewalk locations are indicated by side of road (right, left, or composite (both)). If sidewalks are not present, they are indicated by G in the RDWYSIDE column. In addition, this dataset indicates the type of road access, as well as information related to data inventory dates and when next data update is scheduled. This feature class is derived from event mapping Feature 216, characteristic SIDWLKWD. This data represents the information collected and reported as of the most recent inventory performed and may not reflect the current conditions. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 02/01/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/sidewalks_and_gaps.zip

The Report Covers Long Term Care Software Market Vendors and it is segmented by Product (Electronic Health Records, E-prescribing, Clinical Decision Support Systems, Remote Patient Monitoring Systems, Real-time Location Systems, Billing, Invoicing, and Scheduling Software, and Other Products), Deployment (Cloud Bases and On-premise), End User (Home Healthcare Agencies, Hospice Care Facilities, and Nursing Homes and Assisted Living Facilities), and Geography (North America, Europe, Asia-Pacific, Middle-East and Africa, and South America). The market provides value (in USD million) for the above segments.

Our address datasets contain all geospatial address data of Portugal. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Portugal

Spotzi Targeting users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

• Spotzi Profiling uses the address geographical dataset to create detailed profiles of potential customers. By combining location data with demographic and behavioral information, it can provide insights into the preferences and needs of different customer segments. This can help businesses understand their audience better and create more effective marketing strategies.

• To generate targeted mailing lists, employ our profiling tools to begin delineating your target audience according to demographics, interests, and behaviors. Employ strategies for direct mail customer segmentation to categorize your audience into specific groups. After creating these groups, you can export a list of addresses for focused and effective direct mail campaigns.

• Certainly, you can create address lists by aggregating data based on various parameters such as Portuguese 4- and 7-digit postal code areas. For example, you can generate an address list by postal code, allowing you to target specific geographic regions efficiently. This segmentation strategy aids in tailoring your communication to the unique characteristics of different areas or localities.

• Yes, you can export this Portuguese address list to Excel. Most mailing list databases and management systems offer the option to export data in various formats, including Excel. This facilitates easy integration with other tools and allows for seamless data analysis and manipulation.

• Absolutely. This dataset is well-suited for address validation, offering a comprehensive solution through various means. Utilizing an address validation checker, you can ensure the accuracy and completeness of each address. The address data validation process is further enhanced with our robust API, allowing seamless integration for efficient validation.

  Whether you're validating addresses locally or internationally, our international address validation capabilities cover addresses worldwide. This ensures that your data is reliable, adheres to postal standards, and is ready for successful implementation in diverse global contexts.