Location Intelligence and Location Analytics Market Outlook

The global market size for Location Intelligence (LI) and Location Analytics is projected to grow from $XX billion in 2023 to $XX billion by 2032, exhibiting a CAGR of XX%. This remarkable growth is driven by the increasing adoption of geospatial data in business operations and the rising demand for location-based services in various industries.

One of the primary growth factors for the Location Intelligence and Location Analytics market is the proliferation of Internet of Things (IoT) devices. These devices generate vast amounts of location-based data that can be analyzed to provide valuable insights. Companies are increasingly recognizing the importance of leveraging this data to enhance operational efficiency, improve customer experience, and drive strategic decision-making. The integration of artificial intelligence (AI) and machine learning (ML) with Location Analytics further enhances the ability to process and analyze large datasets, providing more accurate and actionable insights.

Another significant driver is the growing need for real-time location-based services. In sectors such as retail, transportation, and logistics, real-time location analytics enable businesses to track assets, monitor workforce movements, and manage facilities more effectively. This real-time data helps in optimizing routes, reducing fuel consumption, and improving overall productivity. Additionally, the COVID-19 pandemic has accelerated the adoption of location-based services for contact tracing, social distancing monitoring, and ensuring workplace safety, further propelling market growth.

Advancements in geographic information systems (GIS) and the increasing availability of high-resolution satellite imagery are also contributing to market expansion. Modern GIS platforms offer sophisticated tools for spatial analysis, mapping, and visualization, enabling organizations to derive meaningful insights from complex geospatial data. The integration of location analytics with business intelligence (BI) tools allows for comprehensive analysis and visualization of data, leading to better strategic planning and decision-making.

Regionally, North America is expected to hold the largest market share, driven by the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region is anticipated to witness the highest growth rate, fueled by rapid urbanization, increasing investments in smart city projects, and the expanding e-commerce sector. Europe, Latin America, and the Middle East & Africa are also expected to contribute significantly to the market growth, with various industries adopting location-based services to enhance operational efficiency and customer engagement.

Component Analysis

The Location Intelligence and Location Analytics market is segmented into two main components: Software and Services. The Software segment dominates the market, driven by the increasing demand for sophisticated analytics tools that can process and visualize geospatial data. Advanced software solutions offer capabilities such as spatial analysis, mapping, and real-time data processing, enabling businesses to gain deeper insights into their operations and customer behavior. The integration of AI and ML with location analytics software further enhances its analytical capabilities, making it a crucial component for businesses seeking to leverage geospatial data.

Within the Software segment, geographic information systems (GIS) and business intelligence (BI) tools play a pivotal role. GIS platforms provide extensive functionalities for spatial data analysis, mapping, and visualization, allowing organizations to derive actionable insights from complex datasets. The integration of BI tools with location analytics enables businesses to perform comprehensive analyses and generate interactive dashboards, facilitating informed decision-making. The increasing adoption of cloud-based software solutions is also driving market growth, offering scalability, flexibility, and cost-effectiveness to businesses of all sizes.

The Services segment encompasses various professional and managed services that support the deployment and utilization of location analytics solutions. Consulting services assist organizations in identifying their specific needs and developing customized solutions, while implementation services ensure seamless integration of location analytics tools with existing systems. Managed services provide ongoing support, maintenance, and optimization of location analy

Veridion's Location Intelligence Data Suite provides a comprehensive view of where and how businesses operate globally. It delivers granular, structured intelligence across more than 134 million active business entities in 249+ countries and territories. This extensive global coverage includes emerging markets and regions where data from other vendors may be limited. Veridion's data universe covers all companies with a digital footprint.

The Location Intelligence data identifies and maps all operational locations linked to a business, regardless of its legal entity structure. This includes differentiating between the Headquarters (HQ) and all secondary physical locations. Beyond HQs and standard branches, it covers a wide range of specific facility types, including manned and unmanned operational locations. These facilities can be classified into 100+ categories, such as office, factory, warehouse, or retail outlet.

The data also covers subsidiaries and field offices. Veridion is capable of identifying facility types mapped to companies through its granular taxonomy of business activities. Examples of identified facility types include those in energy and utilities (power plants, renewable energy installations, oil and gas facilities), manufacturing hubs, tech centers, supply chain nodes, and specialized facilities like data centers. The classification of facility types is provided across multiple levels of detail, currently supporting Level 1 (L1) in production. Planned enhancements for 2025 include expanding the taxonomy to include Level 2 (L2) with more than 100 unique values and Level 3 (L3) classifications for more precise categorization.

For each location, Veridion provides detailed attributes: - Full Address: Including Country, Region, City, Postcode, Street, and Street Number. - Geo-coordinates: Precise Latitude and Longitude. - Location Type: Classification into 100+ categories. - Operating Metrics: Includes estimated Employee counts and Revenue at the location level. - Business Descriptions & Activity Tags: Descriptions of what is happening at each specific site, including key operations and detailed activity tags. - Operational Status: Tracking whether a location is active or inactive. - Industry Classifications: Location-specific NAICS, SIC, ISIC, and proprietary classifications.

Veridion's location data is built from first-party disclosed information from public web sources such as company websites, social media, press releases, regulatory filings, geolocation services, and media reports. Advanced AI and Natural Language Processing (NLP) technologies are used to extract and structure this information. Proprietary AI models and custom infrastructure process billions of data points. Data triangulation and advanced correlation techniques are employed to tie signals to legal and commercial entities and connect entities across different geographies.

A key differentiator is the High-Frequency Updates. Veridion's entire dataset, including location data, is refreshed weekly. This ensures companies consistently have access to the most accurate and reliable information. Weekly updates capture changes in business locations, operations, address types, functions, and operational status with exceptional speed. Ad hoc updates can also be performed based on specific customer requests.

Data Quality and Accuracy are ensured through a robust, multi-faceted methodology. Veridion consolidates data from multiple sources, resolves data conflicts, and infers missing information. Data is cross-referenced against multiple structured company information sources. Each data point is assigned a confidence score, reflecting the number and reliability of sources used. Only data with sufficient certainty in its accuracy is included. AI-driven models actively detect inconsistencies and refine datasets. Reliability scores for location-level data are assigned based on evaluation of multiple sources. Veridion is developing a system to make these reliability scores directly accessible to customers.

Veridion's location data is linked to the company level. Every operational location is linked to its ultimate parent company, delivering a unified and scalable enterprise hierarchy. This enables cross-border visibility and accurate entity resolution. Location data can be appended with additional company data such as revenue, employees, business description, and ESG data. Since data is collected at the individual entity and facility level, it can be aggregated at portfolio levels without compromising granularity.

Veridion's detailed locational data is critical for various use cases, including: - Supply Chain Risk Management: Provides crucial insights into the geographical distribution of suppliers for risk assessment, compliance checks, and strategic planning. It allows mapping the entire supply chain to identify regions with higher risks, such as areas prone to natural disasters or geopolitical issues. P...

The main variables for this Location dataset are: - Pedestrian influx - Vehicle flow - Resident population - Income level - Business concentration.

Also, the model is enriched with information on the population interested in a specific topic (Like retail store location data), measured from the interaction of users in social networks (Consumer behavior data).

All the variables evaluated in the model are at the spatial grid level, to which it is possible to add existing points of sale and their respective revenue. This additional information makes it possible to estimate the billing of an additional Point of Sale in the best areas identified to locate a specific type of business.

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for location data, consumer behavior data, and store location data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

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).

In the industry of QSRs, data-driven decisions are the key to staying ahead. dataplor's Global QSR Locations Dataset offers an in-depth view of the worldwide QSR landscape using foot traffic patterns and location intelligence that empower businesses with the insights needed to thrive.

Data Points for Precision:

• Brand Profiles: Detailed information on both independent QSRs and multinational chains, including official names, unique identifiers, and specializations.

• Business Classification: Precise categorization by cuisine type (e.g., snack bar, sandwich shop, pizza restaurant) to ensure granular insights.

• Location Precision: Exact street addresses and geographic coordinates for pinpoint mapping and analysis.

• Store Attributes: Comprehensive details such as open/close status to gauge market presence.

Empowering Use Cases:

• Market Entry and Expansion: Identify high-potential markets with unmet demand, and pinpoint optimal locations for new restaurant openings or franchise expansions.

• Competitive Benchmarking: Gain deep insights into competitor strategies, QSR offerings, and geographic trends to inform your own business decisions.

• Targeted Marketing and Promotions: Develop hyper-targeted campaigns based on location demographics, competitor proximity, and local cuisine.

• Supply Chain Optimization: Streamline distribution logistics by understanding restaurant locations, demand fluctuations, and local preferences.

• Investment and Risk Analysis: Evaluate potential investment opportunities in the QSR sector by assessing market saturation, growth potential, and risk factors associated with specific locations and cuisine types.

This comprehensive retail point-of-interest (POI) dataset provides a detailed map of retail establishments across the United States and Canada. Retail strategists, market researchers, and business developers can leverage precise store location data to analyze market distribution, identify emerging trends, and develop targeted expansion strategies.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive retail landscape of location intelligence.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive retail store data database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail store locations -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping centers and malls, and more

Why Choose LocationsXYZ for Your Retail POI Data Needs? At LocationsXYZ, we: -Deliver POI data with 95% accuracy for reliable store location data -Refresh POIs every 30, 60, or 90 days to ensure the most recent retail location information -Create on-demand POI datasets tailored to your specific retail data requirements -Handcraft boundaries (geofences) for shopping center locations to enhance accuracy -Provide retail POI data and polygon data in multiple file formats

Unlock the Power of Retail Location Intelligence With our point-of-interest data for retail stores, you can: -Perform thorough market analyses using comprehensive store location data -Identify the best locations for new retail stores -Gain insights into consumer behavior and shopping patterns -Achieve an edge with competitive intelligence in retail markets

LocationsXYZ has empowered businesses with geospatial insights and retail location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge retail POI data and shopping center location intelligence.

LinkedIn companies use datasets to access public company data for machine learning, ecosystem mapping, and strategic decisions. Popular use cases include competitive analysis, CRM enrichment, and lead generation.

Use our LinkedIn Companies Information dataset to access comprehensive data on companies worldwide, including business size, industry, employee profiles, and corporate activity. This dataset provides key company insights, organizational structure, and competitive landscape, tailored for market researchers, HR professionals, business analysts, and recruiters.

Leverage the LinkedIn Companies dataset to track company growth, analyze industry trends, and refine your recruitment strategies. By understanding company dynamics and employee movements, you can optimize sourcing efforts, enhance business development opportunities, and gain a strategic edge in your market. Stay informed and make data-backed decisions with this essential resource for understanding global company ecosystems.

Dataset Features

• timestamp: Represents the date and time when the company data was collected.
• id: Unique identifier for each company in the dataset.
• company_id: Identifier linking the company to an external database or internal system.
• url: Website or URL for more information about the company.
• name: The name of the company.
• about: Brief description of the company.
• description: More detailed information about the company's operations and offerings.
• organization_type: Type of the organization (e.g., private, public).
• industries: List of industries the company operates in.
• followers: Number of followers on the company's platform.
• headquarters: Location of the company's headquarters.
• country_code: Code for the country where the company is located.
• country_codes_array: List of country codes associated with the company (may represent various locations or markets).
• locations: Locations where the company operates.
• get_directions_url: URL to get directions to the company's location(s).
• formatted_locations: Human-readable format of the company's locations.
• website: The official website of the company.
• website_simplified: A simplified version of the company's website URL.
• company_size: Number of employees or company size.
• employees_in_linkedin: Number of employees listed on LinkedIn.
• employees: URL of employees.
• specialties: List of the company’s specializations or services.
• updates: Recent updates or news related to the company.
• crunchbase_url: Link to the company’s profile on Crunchbase.
• founded: Year when the company was founded.
• funding: Information on funding rounds or financial data.
• investors: Investors who have funded the company.
• alumni: Notable alumni from the company.
• alumni_information: Details about the alumni, their roles, or achievements.
• stock_info: Stock market information for publicly traded companies.
• affiliated: Companies or organizations affiliated with the company.
• image: Image representing the company.
• logo: URL of the official logo of the company.
• slogan: Company’s slogan or tagline.
• similar: URL of companies similar to this one.

Distribution

• Data Volume: 56.51M rows and 35 columns.
• Structure: Tabular format (CSV, Excel).

Usage

This dataset is ideal for:
- Market Research: Identifying key trends and patterns across different industries and geographies.
- Business Development: Analyzing potential partners, competitors, or customers.
- Investment Analysis: Assessing investment potential based on company size, funding, and industries.
- Recruitment & Talent Analytics: Understanding the workforce size and specialties of various companies.

Coverage

• Geographic Coverage: Global, with company locations and headquarters spanning multiple countries.
• Time Range: Data likely covers both current and historical information about companies.
• Demographics: Focuses on company attributes rather than demographics, but may contain information about the company's workforce.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For building models, conducting research, or enhancing machine learning algorithms with business data.
• Researchers: For academic analysis in fields like economics, business, or technology.
• Businesses: For analysis, competitive benchmarking, and strategic development.
• Investors: For identifying and evaluating potential investment opportunities.

Dataset Name Ideas

• Global Company Profile Database
• **Business Intellige

The dataset can be used in bundle with other sources to increase the accuracy of existing datasets related to traffic or marketing intelligence. For example to better understand the route users takes inside the cities, or which block surrounding the centre receives the majority of passages. Or they could be interpolated to confirm or obtain brand new trends.

Immagine having a customer asking which city area could be the most desirable for Advertising boards, knowing not only the general traffic conditions but also the passages into hot spots such as limited traffic zone entrances (an hot spot where usually drivers keep low speed to pay attention to the surrouding area) will sure help give a over the top service.

Our Point Of Interest (POI) Data links people's movements to over 14 million physical locations worldwide. This aggregated and anonymized data provides context for visit volumes and patterns, compiled from diverse global sources.

Reach

We calculate POI, Place, and OOH level insights using Factori's Mobility & People Graph data from multiple sources. To attribute foot traffic accurately, we combine specific attributes such as location ID, day of the week, and time of day, yielding up to 40 possible data records for a single POI. This method ensures precise location intelligence data.

Data Export Methodology

Our dynamic data collection process ensures the most up-to-date information and insights are delivered at optimal intervals, whether daily, weekly, or monthly.

Use Cases

Point Of Interest (POI) Data is invaluable for credit scoring, retail analytics, market intelligence, and urban planning, providing a robust foundation for data-driven decision-making and strategic planning.

Techsalerator’s Location Sentiment Data for Monaco

Techsalerator’s Location Sentiment Data for Monaco provides a comprehensive dataset that captures real-time public sentiment across various locations. This dataset is essential for businesses, researchers, and policymakers looking to analyze regional sentiments, consumer behavior, and market trends in Monaco.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Monaco

Techsalerator’s Location Sentiment Data for Monaco delivers structured insights into public sentiment by analyzing online and offline feedback from various geographic areas. This dataset is crucial for market research, tourism analytics, event monitoring, and business intelligence.

Top 5 Key Data Fields

• Geographic Location – Identifies the specific area in Monaco where sentiment data is collected, providing hyper-local insights.
• Sentiment Score – Measures positive, neutral, or negative sentiment based on social media, reviews, and other public data sources.
• Industry-Specific Sentiment – Categorizes sentiment across various industries such as tourism, hospitality, luxury goods, and finance.
• Trending Topics and Keywords – Highlights the most discussed topics in each location, offering valuable insights for businesses and researchers.
• Temporal Analysis – Tracks sentiment trends over time, helping businesses and policymakers understand seasonal or event-driven sentiment shifts.

Top 5 Location Sentiment Trends in Monaco

• Tourism and Luxury Sentiment – Analyzing visitor reviews and social media reveals how tourists perceive Monaco’s luxury offerings, from hotels to casinos.
• Event-Based Sentiment Fluctuations – Major events like the Monaco Grand Prix and Yacht Show significantly impact public sentiment in key locations.
• Real Estate and Investment Perception – Tracking investor sentiment on property markets and financial services helps businesses make data-driven decisions.
• Retail and Consumer Preferences – Evaluating customer sentiment toward Monaco’s high-end shopping districts provides insights for retail strategies.
• Sustainability and Environmental Sentiment – Monitoring public opinion on Monaco’s eco-friendly initiatives and green policies aids in sustainability planning.

Top 5 Applications of Location Sentiment Data in Monaco

• Tourism and Hospitality Optimization – Understanding visitor feedback helps hotels, restaurants, and entertainment venues improve services.
• Event Impact Analysis – Measuring sentiment changes before, during, and after major events assists in future event planning and marketing.
• Luxury Brand Marketing – High-end brands can use sentiment analysis to refine their customer engagement and advertising strategies.
• Urban Planning and Policy Making – Government agencies leverage sentiment data to enhance public services and infrastructure.
• Financial Market Insights – Investors and analysts use sentiment trends to gauge public confidence in Monaco’s financial sector.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Monaco, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Location
• Sentiment Score
• Industry-Specific Sentiment
• Trending Topics and Keywords
• Temporal Analysis
• Social Media and Review Aggregation
• Consumer Behavior Insights
• Luxury and Retail Sentiment
• Tourism and Hospitality Trends
• Contact Information

For businesses, researchers, and policymakers seeking in-depth sentiment insights, Techsalerator’s dataset offers a powerful tool to understand and act on public perception trends in Monaco.

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Business Intelligence (BI) And Analytics Platforms Market Size 2025-2029

The business intelligence (BI) and analytics platforms market size is forecast to increase by USD 20.67 billion at a CAGR of 8.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing need to enhance business efficiency and productivity. This trend is particularly prominent in industries undergoing digital transformation, seeking to gain a competitive edge through data-driven insights. Furthermore, the burgeoning medical tourism industry worldwide presents a lucrative opportunity for BI and analytics platforms, as healthcare providers and insurers look to optimize patient care and manage costs. However, this market faces challenges as well.
The BI and analytics platforms market is characterized by its potential to revolutionize business operations and improve decision-making, while also presenting challenges related to data security and privacy. Companies looking to capitalize on this market's opportunities must prioritize both innovation and robust security measures to meet the evolving needs of their clients. Ensuring data confidentiality and compliance with evolving regulations is crucial for companies to maintain trust with their clients and mitigate potential risks.

What will be the Size of the Business Intelligence (BI) And Analytics Platforms Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, data integration tools play a crucial role in seamlessly merging data from various sources. Statistical modeling and machine learning algorithms are employed for deriving insights from this integrated data. Data security tools ensure the protection of sensitive information, while decision automation streamlines processes based on data-driven insights. Data discovery tools enable users to explore and understand complex data sets, and deep learning frameworks facilitate advanced analytics capabilities. Semantic search and knowledge graphs enhance data accessibility, and dashboarding tools provide real-time insights through interactive visualizations. Metadata management tools and data cataloging help manage vast amounts of data, while data virtualization tools offer a unified view of data from multiple sources.
Graph databases and federated analytics enable advanced data querying and analysis. AI-driven insights and augmented analytics offer more accurate predictions through predictive modeling and what-if analysis. Scenario planning and geospatial analytics provide valuable insights for strategic decision-making. Cloud data warehouses and streaming analytics facilitate real-time data ingestion and processing, and database administration tools ensure data quality and consistency. Edge analytics and cognitive analytics offer decentralized data processing and advanced contextual understanding, respectively. Data transformation techniques and location intelligence add value to raw data, making it more actionable for businesses. A data governance framework ensures data compliance and trustworthiness, while explainable AI (XAI) and automated reporting provide transparency and ease of use.

How is this Business Intelligence (BI) and Analytics Platforms Industry segmented?

The business intelligence (BI) and analytics platforms industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  BFSI
  Healthcare
  ICT
  Government
  Others


Deployment

  On-premises
  Cloud


Business Segment

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The BFSI segment is estimated to witness significant growth during the forecast period. The market is witnessing significant growth in the BFSI sector due to the complete digitization of core business processes and the adoption of customer-centric business models. With the emergence of new financial technologies such as cashless banking, phone banking, and e-wallets, an extensive amount of digital data is generated every day. Analyzing this data provides valuable insights into system performance, customer behavior and expectations, demographic trends, and future growth areas. Business intelligence dashboards, in-memory analytics, anomaly detection, decision support systems, and KPI dashboards are essential tools used in the BFSI sector for data analysis. ETL processes, data governance, mobile BI, and forecast accuracy are other critical components of BI and a

Xtract.io's massive point-of-interest database represents a transformative resource for location intelligence across the United States and Canada. Big data analysts, market researchers, and strategic planners can utilize these comprehensive location insights to develop sophisticated market strategies, conduct advanced spatial analysis, and gain a deep understanding of regional geographical landscapes.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

The refugee location data (Geo-Refugee) provides information on the geographical locations, population sizes and accommodation types of refugees and people in refugee-like situations throughout Africa. Based on the United Nations High Commissioner for Refugees' Location and Demographic Composition data as well as information contained in supplemental UNHCR resources, Geo-Refugee assigns administrative unit names and geographic coordinates to refugee camps/ centers, and locations hosting dispersed (self-settled) refugees. Geo-Refugee was collected for the purpose of investigating the relationship between refugees and armed conflict, but can be used for a number of refugee-related studies. The original data for the category refugees and people in a refugee-like situation by accommodation type and location name comes directly from the UNHCR. The category refugees includes: "individuals recognized under the 1951 Convention relating to the Status of Refugees and its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR statute; individuals granted complementary forms of protection and those enjoying temporary protection.The category people in a refugee-like situation "is descriptive in nature and includes groups of people who are outside their country of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained" (UNHCR http://www.unhcr.org/45c06c662.html). The unit of the data is the first-level administrative unit (province, region or state). A refugee location is defined as a unit with a known refugee population, as established by UNHCR country offices. The locations data was compiled using statistics provided by the UNHCR Division of Programme Support and Management. Several of the refugee sites in the original UNHCR data are camp names or other lo cations which are not immediately traceable to a particular location using even the most established geographical databases like that of the National Geospatial Intelligence Agency (NGA). Thus, unit-level location of refugees was established and confirmed using supplementary resources including reports, maps, and policy documents compiled by the UNHCR and contained in the Refworld database (see http://www.unhcr.org/cgi-bin/texis/vtx/refworld/rwmain). Refworld was the primary database used for this project. Geographic coordinates were assigned using the database of the National Geospatial-Intelligence Agency. See https://www1.nga.mil/Pages/default.aspx for more information. All attempts were made to find precise coordinates, including cross-referencing with Google Maps. The current version of the data covers 43 African countries and encompasses the period 2000 to 2010. The UNHCR began systematically collecting information on the locations and demographic compositions of refugee populations in 2000.

Mobility data is collected through location-aware mobile apps using an SDK-based implementation. Users explicitly consent to share their location data via a clear opt-in process and are provided with clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure the highest quality data is available for analysis.

• Record Count: 90 Billion
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Mobility Data Reach

Our data reach encompasses the total counts available across various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology

We collect data dynamically, offering the most updated data and insights at the best-suited intervals (daily, weekly, monthly, or quarterly).

Business Needs

Our data supports various business needs, including consumer insight, market intelligence, advertising, and retail analytics.

Comprehensive dataset of 78 Sports activity locations in Gyeongsangnam-do, South Korea as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Techsalerator’s Location Sentiment Data for Hungary

Techsalerator’s Location Sentiment Data for Hungary provides a deep analysis of public sentiment across various geographic locations. This dataset is essential for businesses, researchers, and policymakers looking to understand emotional trends and opinions tied to specific areas in Hungary. The data enables insights into customer experiences, regional mood variations, and emerging social trends.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Hungary

Techsalerator’s Location Sentiment Data for Hungary delivers structured insights into how people feel about locations, businesses, and public spaces. It is an invaluable tool for market research, urban planning, brand reputation analysis, and customer engagement strategies.

Top 5 Key Data Fields

• Geographic Location – Captures the precise location (city, district, or coordinates) associated with sentiment data.
• Sentiment Score – Measures the overall sentiment (positive, neutral, negative) based on text, reviews, and social media data.
• Emotion Analysis – Categorizes emotions such as happiness, anger, frustration, or excitement tied to a specific place.
• Data Source Type – Identifies whether the sentiment originates from social media, reviews, surveys, or other public data sources.
• Time of Sentiment Capture – Records the timestamp of sentiment data to analyze changes over time.

Top 5 Location Sentiment Trends in Hungary

• Tourism Hotspots Sentiment – Popular tourist destinations like Budapest, Lake Balaton, and Eger show fluctuating sentiment influenced by seasonal trends and visitor experiences.
• Retail & Shopping Experiences – Sentiment analysis in malls and retail areas provides insights into customer satisfaction and emerging market trends.
• Public Transport Perception – Analyzing social sentiment around Budapest's metro, trams, and buses helps in assessing public satisfaction and areas needing improvement.
• Real Estate and Neighborhood Sentiment – Data reveals how residents and newcomers perceive different districts, impacting housing trends and urban development.
• Event-Based Sentiment Fluctuations – Major events such as festivals, sports matches, and political gatherings significantly affect local sentiment dynamics.

Top 5 Applications of Location Sentiment Data in Hungary

• Business Intelligence & Customer Insights – Companies leverage sentiment data to optimize store locations, improve customer service, and refine marketing strategies.
• Urban & Infrastructure Planning – City planners use sentiment trends to enhance public spaces, transportation, and safety measures.
• Brand & Reputation Management – Businesses analyze location-based sentiment to track public perception and mitigate negative feedback.
• Political & Social Analysis – Researchers use sentiment data to assess public opinion on policies, elections, and governance.
• Tourism & Hospitality Enhancement – Hotels, restaurants, and tourism boards use sentiment insights to improve guest experiences and service offerings.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Hungary, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Location
• Sentiment Score
• Emotion Analysis
• Data Source Type
• Time of Sentiment Capture
• Social Media Mentions
• Review-Based Sentiment
• Event-Based Sentiment
• Location-Based Trends
• Contact Information

For an in-depth understanding of location sentiment trends in Hungary, Techsalerator’s dataset is a vital resource for businesses, researchers, marketers, and urban planners.

LocBench contains 7 geo-aware image classification datasets and 4 geo-aware image regression datasets, aiming to systematically compare the location encoders' performance and their impact on the model's overall geographic bias.Geo-aware image classification datasets: BirdSnap, BirdSnap†, NABirds†, iNat2017, iNat2018, YFCC, and fMoW. Please note that BirdSnap and BirdSnap† are in the same file birdsnap.tar.gz. iNat2017 and iNat2018 are not stored here due to storage limitations but can be accessed via Dropbox.Geo-aware image regression datasets: MOSAIKS (Population Density, Forest Cover, Nightlight Luminosity, and Elevation), and SustainBench (Asset Index, Women BMI, Water Index, Child Mortality Rate, Sanitation Index, and Women Edu). The SustainBench datasets can be downloaded from Google Drive.All data products created through our work that are not covered under upstream licensing agreements are available via a CC BY-NC 4.0 license. All upstream data use restrictions take precedence over this license.

This dataset contains tweets about climate change that were collected using the Tweepy library. The dataset is available for natural language processing (NLP) to determine the sentiment of the tweets, either positive, negative, or neutral. The dataset also includes information about the location of the tweets and the topics that are discussed

This dataset is a valuable resource for researchers, data scientists and policymakers who are interested in public opinion about climate change and the Biden administration's climate change policies. The dataset can be used to track public sentiment, identify key topics, and measure the impact of the Biden administration's policies.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Overview The OYO Hotel Rooms Dataset provides comprehensive data on hotel room listings from OYO, covering various attributes related to pricing, amenities, and customer ratings. This dataset is valuable for researchers, data scientists, and machine learning practitioners interested in hospitality analytics, price prediction, customer satisfaction analysis, and clustering-based insights.

Data Source The dataset has been collected from publicly available OYO hotel listings and includes structured information for analysis.

Features The dataset consists of multiple attributes that define each hotel room, including:

Hotel Name: The name of the hotel property. City: The location where the hotel is situated. Room Type: Category of the room (e.g., Standard, Deluxe, Suite). Price (INR): The cost per night in Indian Rupees. Discounted Price: The price after applying discounts. Rating: The customer rating for the hotel (out of 5). Reviews: The number of customer reviews. Amenities: A list of available facilities such as WiFi, AC, Breakfast, Parking, etc. Latitude & Longitude: Geolocation details for mapping and spatial analysis. Potential Use Cases Price Prediction: Using regression models to predict hotel room pricing. Customer Sentiment Analysis: Analyzing ratings and reviews to understand customer satisfaction. Market Segmentation: Clustering hotels based on price, rating, and location. Recommendation Systems: Building personalized hotel recommendations. File Format

OYO_HOTEL_ROOMS.xlsx (Excel format) – Contains structured tabular data.

Acknowledgment This dataset is intended for academic and research purposes. The data is sourced from publicly available hotel listings and does not contain any personally identifiable information.