The main purposes of this online map are 1. to demonstrate the Web-Based Geographic Information System (GIS) in the District of Columbia Office of Tax and Revenue (OTR) Real Property Tax Administration (RPTA), and 2. to share detailed real property data and information to real property owners, the public, and other government entities. The rich map and interactive application include relevant real property valuation contributing map layers, links to original source agencies, and a variety of search, query, and analysis options to meet the needs of a wide user base. The location and links to the original DC Boundary Stones add a fun, historical,and educational component.The Office of the Chief Financial Officer, DC Office of Tax and Revenue (OTR), Real Property Assessment Division values all real property in the District of Columbia. The public interactive online DC Office of Tax and Revenue Real Property Assessment Lot Map Search application accompanies the OTR Tax Payer Service Center and may be used to search for and view all real property, related assessment areas, assessment data, and detailed assessment information.

Revenue maps for different hoblis of Bengaluru. Each map is of one hobli. Each hobli is parceled into survey numbers and these maps help you zoom into and look at the survey numbers at Hobli level.

The DC Office of the Chief Financial Officer (OCFO), Office of Tax and Revenue (OTR), Real Property Tax Administration (RPTA) values all real property in the District of Columbia. This public interactive Real Property Assessment map application accompanies the OCFO MyTax DC and OTR websites. Use this mapping application to search for and view all real property, assessment valuation data, assessment neighborhood areas and sub-areas, detailed assessment information, and many real property valuation reports by various political and administrative areas. View by other administrative areas such as DC Wards, ANCs, DC Squares, and by specific real property characteristics such as property type and/or sale date. If you have questions, comments, or suggestions regarding the Real Property Assessment Map, contact the Real Property Assessment Division GIS Program at (202) 442-6484 or maps.title@dc.gov.

This feature layer provides digital tax parcels for the Organized Towns of the State of Maine. Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. "Maine Parcels Organized Towns Feature" and "Maine Parcels Organized Towns ADB" are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions. In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. "Maine Parcels Unorganized Territory Feature" is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services (https://www.maine.gov/revenue/taxes/property-tax) to compile up-to-date parcel ownership information. Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.

This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (Maine GeoLibrary). Grant recipient parcel data submissions were guided by standards presented to the Maine GeoLibrary Board on May 21, 2005, which are outlined in the "Standards for Digital Parcel Files" document available on the Maine GeoLibrary publications page (https://www.maine.gov/geolib/policies/standards.html). This dataset also contains municipal parcel data acquired through other sources; the data sources are identified (where available) by the field “FMSCORG”. Note: Join this feature layer with the "Maine Parcels Organized Towns ADB" table (https://maine.hub.arcgis.com/maps/maine::maine-parcels-organized-towns-feature/about?layer=1) for available ownership information. A date field, “FMUPDAT”, is attributed with the most recent update date for each individual parcel if available. The "FMUPDAT" field will not match the "Updated" value shown for the layer. "FMUPDAT" corresponds with the date of update for the individual data, while "Updated" corresponds with the date of update for the ArcGIS Online layer as a whole. Many parcels have not been updated in several years; use the "FMUPDAT" field to verify currency.

To access the tax lot layer you will need to contact the county Assessor's office.

ORMAP is a statewide digital cadastral base map that is publicly accessible, continually maintained, supports the Oregon property tax system, supports a multi-purpose land information system, strives to comply with appropriate state and national standards, and will continue to be improved over time.

Global High Precision Smart Travel Digital Map market size 2025 was XX Million. High Precision Smart Travel Digital Map Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

According to Cognitive Market Research, the global Mind Mapping Tool market size will be USD 5124.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 2049.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1537.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1178.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 256.23 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 102.49 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
The Cloud-based category is the fastest growing segment of the Mind Mapping Tool industry

Market Dynamics of Mind Mapping Tool Market

Key Drivers for Mind Mapping Tool Market

Increasing Adoption of Digital Tools for Enhanced Productivity and Organization to Boost Market Growth

The increasing adoption of digital tools for enhanced productivity and organization has been a major driver in many industries, especially in the context of digital transformation. Digital tools enable businesses and individuals to automate repetitive tasks, streamline workflows, and manage time more effectively, significantly enhancing overall productivity. Although many digital tools offer long-term savings, the initial investment in licenses, training, and setup can be a barrier, especially for small businesses or individuals with limited resources. While the adoption of digital tools offers significant benefits, such as enhanced productivity, cost savings, and improved collaboration, businesses must carefully manage the associated challenges, including high costs, security concerns, and integration complexities. Addressing these restraints while leveraging the key drivers will be essential for organizations looking to integrate digital tools into their operations successfully.

Rising demand for visual representation to improve brainstorming and creativity

The rising demand for visual representation to improve brainstorming and creativity is driven by several key factors. Visual tools enhance information retention and idea generation, making complex concepts easier to understand. The growing adoption of digital collaboration platforms fuels this demand as remote teams seek efficient ways to share ideas. Additionally, the increased focus on innovation and creative problem-solving in businesses accelerates the use of visual aids like mind maps, flowcharts, and interactive diagrams. This trend is further supported by advancements in software tools that allow real-time visualization, fostering greater collaboration, faster decision-making, and enhanced creativity.

Restraint Factor for the Mind Mapping Tool Market

Expensive software integration limits widespread adoption in businesses

The high cost of software integration is a major restraint limiting widespread adoption among businesses. The initial expenses, including licensing, implementation, and ongoing maintenance, can be prohibitive, especially for small to medium-sized enterprises (SMEs). Additionally, the need for specialized personnel and training further adds to the financial burden. Businesses may delay or avoid adoption to allocate resources elsewhere, affecting their ability to remain competitive. The complexity and time required to integrate new software into existing systems can also deter companies, as it may disrupt operations and incur additional costs, limiting scalability and overall ROI.

Impact of Covid-19 on the Mind Mapping Tool Market

The COVID-19 pandemic significantly accelerated the adoption of mind-mapping tools. Remote work and online learning necessitated efficient tools for brainstorming, note-taking, and project management. Mind mapping tools offer a visual and intuitive way to organize information, collaborate remotely, and enhance productivity. This surge in demand drove mar...

In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

The Maine Geoparcel Viewer Application allows users to search and view available digital parcel data for Organized Townships and Unorganized Territories in the State of Maine. The Maine GeoLibrary and the Maine Office of GIS do not maintain parcel data for communities, cannot verify parcel ownership, and are not responsible for individual parcel data verification or updating emergency records concerning parcel addresses. If you have questions about a specific parcel, please contact the appropriate Town Office or County Registry of Deeds for the most up-to-date information.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. The "Maine Parcels Organized Towns Feature" layer and "Maine Parcels Organized Towns ADB" table are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Please contact the appropriate Town Office or the County Registry of Deeds for more up-to-date parcel information. Organized Town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), Maine Revenue Services is the authoritative source for parcel data. The "Maine Parcels Unorganized Territory" layer is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services to compile up-to-date parcel ownership information.

The 3D Mapping and 3D Modelling Software Market is estimated to be valued at USD 5.16 Billion in 2022 and is expected to reach USD 16.26 Billion by 2030, registering a CAGR of 15.4% during a forecast period of 2023-2030. What are the factors impacting the growth of 3D Mapping and 3D Modelling Software Market?

3D-enabled display devices for advanced and better navigation are increasing the demand for 3D Mapping and 3D Modelling Market 

The increasing need for HD experience is anticipated to spike the development of 3D maps. 3D technology in previous years available to the users was not that satisfactory. Consumers need the finest viewing experience of perceived 3D pictures that look like things. 3D mapping and 3D modeling provide real-life experiences of the surrounding buildings and landscape by seeing them through 3D-enabled devices like tablets, smartphones, and personal computers are projected to rise in the upcoming years. The increasing development in technology, more knowledge of advanced products, and changing lifestyles are surging the demand for 3D-enabled gadgets. Furthermore, the growing need for crisp and realistic picture representation, outstanding 3D effects, and an exceptional mapping and navigation experience is propelling the 3D mapping and 3D modeling market.

Rising corruption and theft concerns are the hurdles to the growth of the 3D Mapping and 3D Modeling Software Market.

The animation industry is still vulnerable to corruption and piracy. Companies' software installations are targeted, and pirated copies are sold on the black market. As a result, the industry suffers massive financial losses. Companies have developed surveillance and monitoring techniques to prevent illicit downloads of 3D mapping and modeling software in order to combat piracy. As a result, people have been encouraged to use lawful digital content. In recent years, government policies and regulatory reforms have been put in place to combat piracy. However, adaptable business plans are required to establish mitigation methods and to take proactive steps such as forming anti-piracy cells and promoting awareness. Moreover, in many countries, there is only one policy to avoid theft is to restrict the sites and penalties to illegal users. Thus, theft is the major hurdle in the growth of the 3D Mapping and 3D Modeling Software Market.

Impact of COVID-19 on the 3D Mapping and 3D Modelling Software Market:

The outbreak of the COVID-19 pandemic has increased the consumer demand for 3D mapping and 3D modeling software. Logistics, online learning, healthcare, e-commerce, and other various online business, collaborations experienced significant expansion, well exceeding the limits of their internal and customer-facing applications. For example, iMap9 is a floor-cleaning robot that can explore and clean floors without the need for human assistance. It uses 3D mapping technology to clean the floors. To manage huge volumes of geographical data while satisfying customer requirements, organizations deploy 3D mapping and modeling software solutions. What is 3D Mapping and 3D Modelling Software?

3D mapping software uses machine vision to help in profiling objects in 3D to map them with the real world, offering the recent technical methods, giving the most advanced technical approaches for visualization and information collecting.3D mapping imaging technology and other plenoptic techniques are also utilized to create the 3D effects by finding the light field. 3D modeling is the method of creating a mathematical representation of a three-dimensional object using software. The resulting product is called a 3D model, and these 3-dimension models are used in various different industries. Increasing demand for 3D animation in mobile applications and the development of 3D-enabled display devices for advanced and better navigation are boosting the growth of the 3D mapping and 3D modeling software market.

In the most recently reported fiscal year, Alphabet's revenue amounted to 350 billion U.S. dollars, up from 307.3 billion U.S. dollars in the previous year. Alphabet Inc. is an American multinational conglomerate created in 2015 as the parent company of Google, along with several other companies previously owned by or tied to Google. Alphabet business segments Alphabet has several operating segments, including Google and Other Bets. Google is the only reportable segment, as none of the other segments meet the quantitative revenue thresholds to qualify as reportable segments. In 2024, Google segment revenue amounted to 348.1 billion U.S. dollars. During the same fiscal period, the Other Bets segment revenue amounted to 1.64 billion U.S. dollars in total. According to the company, the Google product segment includes “revenues from the company’s main products Ads, Android, Chrome, Google Cloud, Google Maps, digital content distribution platform Google Play, Hardware, Search and YouTube. Google generates revenues primarily from advertising; sales of apps, in-app purchases, digital content products, and hardware; and licensing and service fees, including fees received for Google Cloud offerings.” Alphabet’s Other Bets segment mainly generates revenues through the sales of TV and internet services through Access, in addition to R&D services and licensing through Verily. Alphabet businesses Calico, CapitalG, GV, Waymo and X are also included in this segment.

The UAS Facility Maps are designed to identify permissible altitudes (above ground level) at which UAS, operating under the Small UAS Rule (14 CFR 107), can be authorized to fly within the surface areas of controlled airspace. These altitude parameters, provided by the respective air traffic control facilities, are criteria used to evaluate airspace authorization requests (14 CFR 107.41), submitted via FAA.GOV/UAS. Airspace authorization requests for altitudes in excess of the predetermined map parameters will require a lengthy coordination process. This dataset will be continually updated and expanded to include UAS Facility Maps for all controlled airspace by Fall 2017. This map is not updated in real time. Neither the map nor the information provided herein is guaranteed to be current or accurate. Reliance on this map constitutes neither FAA authorization to operate nor evidence of compliance with applicable aviation regulations in or during enforcement proceedings before the National Transportation Safety Board or any other forum. Disclaimer of Liability. The United States government will not be liable to you in respect of any claim, demand, or action—irrespective of the nature or cause of the claim, demand, or action—alleging any loss, injury, or damages, direct or indirect, that may result from the use or possession of any of the information in this draft map or any loss of profit, revenue, contracts, or savings or any other direct, indirect, incidental, special, or consequential damages arising out of any use of or reliance upon any of the information in this draft map, whether in an action in contract or tort or based on a warranty, even if the FAA has been advised of the possibility of such damages. The FAA’s total aggregate liability with respect to its obligations under this agreement or otherwise with respect to the use of this draft map or any information herein will not exceed $0. Some States, Territories, and Countries do not allow certain liability exclusions or damages limitations; to the extent of such disallowance and only to that extent, the paragraph above may not apply to you. In the event that you reside in a State, Territory, or Country that does not allow certain liability exclusions or damages limitations, you assume all risks attendant to the use of any of the information in this draft map in consideration for the provision of such information. Export Control. You agree not to export from anywhere any of the information in this draft map except in compliance with, and with all licenses and approvals required under, applicable export laws, rules, and regulations. Indemnity. You agree to indemnify, defend, and hold free and harmless the United States government from and against any liability, loss, injury (including injuries resulting in death), demand, action, cost, expense, or claim of any kind or character, including but not limited to attorney’s fees, arising out of or in connection with any use or possession by you of this draft map or the information herein. Governing Law. The above terms and conditions will be governed by the laws of each and every state within the United States, without giving effect to that state’s conflict-of-laws provisions. You agree to submit to the jurisdiction of the state or territory in which the relevant use of any of the information in this draft map occurred for any and all disputes, claims, and actions arising from or in connection with this draft map or the information herein.

Table View of Master_OP_REV - including Budgets and Actuals from FY 2016, 2017, 2018, 2019, and FYTD 2020. This View is the data source for Revenue Dashboards. Update schedule: Once per Month.

This layer shows median earnings by occupational group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24021Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Introduction

Google Statistics: Google is one of the biggest technology companies in the world. It was founded in 1998 by Larry Page and Sergey Brin. Google started as a search engine, helping people find information online. Over time, it grew into a company that offers many services like Gmail, YouTube, Google Maps, and Android. It also develops new technology, such as artificial intelligence and cloud computing.

Google’s mission is to make information easy to access for everyone. Today, billions of people use Google’s services daily. The company continues to innovate and improve technology to make life easier for users worldwide.

This Esri File GeoDatabase (FGDB) contains digital tax parcel data for Maine Organized Towns and includes the following: Parcels (feature layer); Parcels_ADB (table); and GEOCODES (table).Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. Maine Parcels Unorganized Territory is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of the Maine Revenue Service to compile up-to-date parcel ownership information.Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (MLGI). Grant recipient parcel data submissions were guided by standards presented to the MLGI Board on May 21, 2005,outlined in "Standards for Digital Parcel Files".GEOCODES is a table that lists standardized names and unique identifiers for Maine minor civil divisions and reservations, which represents the first official Standard Geographic Code endorsed and adopted by the Governor of Maine, on July 1, 1971.

This web map shows the locations and details of Inland Revenue Department's offices in Hong Kong. It is a set of data made available by the Inland Revenue Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("Hong Kong CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk .

The eCommerce activity of PT. MAP Active Adiperkasa Tbk. amounted to US$8m in 2024. Learn more about their online business including detailed eCommerce revenue analytics.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1Rvsb53lfYA00A2PJU22lSR44EBgtAThdeDrdhl_NGJk/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for corporate intelligence, market research, and lead generation.

Canaria’s Indeed Company Profile Data offers comprehensive information on over 5 million US companies, refreshed quarterly or biannually, with more frequent updates available for select subsets. This dataset captures essential company attributes, enabling actionable insights for data-driven business strategies.

Each company profile includes AI-validated attributes to ensure consistency and accuracy across the dataset: - Core Company Information: Access details on company name, industry, website, and unique identifiers, covering both headquarters and branch locations. - Leadership Details: Includes information on key executives such as the CEO, sourced from platform data and validated for precision. - Digital Presence: Links to company websites, profile URLs, and industry pages offer an expanded view of the company’s online footprint, supporting deeper analysis. - Company Metrics: Follower counts, ratings, and review metrics allow for assessment of a company’s public engagement and social influence. - Financial & Demographic Data: Company size, revenue range, employee counts, and founding year provide a complete view of each company’s market footprint.

Optional Google Maps Enrichment For enhanced geographic accuracy, Canaria’s optional Google Maps enrichment table includes branch-level insights like location coordinates, hours of operation, contact details, and business category. This additional data layer is ideal for location-based strategies that require precise branch data.

Core Industry Applications - Corporate Intelligence: Develop targeted insights on company growth, market positioning, and industry alignment for strategic planning. - Market Research: Leverage company size, revenue, and industry data for comprehensive views of U.S. and global market trends. - Lead Generation: Identify high-value leads using company size, industry, and location data, ideal for targeted outreach. - Account-Based Marketing (ABM): Tailor marketing efforts based on company demographics, enhancing the relevance and effectiveness of campaigns. Competitive Analysis: Assess competitor profiles, including digital presence and follower metrics, to inform competitive strategy.

Combining high-quality AI-driven insights with human validation, Canaria’s Indeed Company Profile Data provides a reliable foundation for data-driven decision-making across HR, sales, and corporate strategy. Whether sourced from LinkedIn, Indeed, or Google Maps, our data offers flexible delivery and integration options tailored to support your unique business needs.

This feature layer provides digital tax parcels for the Organized Towns of the State of Maine. Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. "Maine Parcels Organized Towns Feature" and "Maine Parcels Organized Towns ADB" are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions. In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. "Maine Parcels Unorganized Territory Feature" is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services (https://www.maine.gov/revenue/taxes/property-tax) to compile up-to-date parcel ownership information. Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.

This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (Maine GeoLibrary). Grant recipient parcel data submissions were guided by standards presented to the Maine GeoLibrary Board on May 21, 2005, which are outlined in the "Standards for Digital Parcel Files" document available on the Maine GeoLibrary publications page (https://www.maine.gov/geolib/policies/standards.html). This dataset also contains municipal parcel data acquired through other sources; the data sources are identified (where available) by the field “FMSCORG”. Note: Join this feature layer with the "Maine Parcels Organized Towns ADB" table (https://maine.hub.arcgis.com/maps/maine::maine-parcels-organized-towns-feature/about?layer=1) for available ownership information. A date field, “FMUPDAT”, is attributed with the most recent update date for each individual parcel if available. The "FMUPDAT" field will not match the "Updated" value shown for the layer. "FMUPDAT" corresponds with the date of update for the individual data, while "Updated" corresponds with the date of update for the ArcGIS Online layer as a whole. Many parcels have not been updated in several years; use the "FMUPDAT" field to verify currency.