Summary:

A sub-set of the Gaia Data Release 3 data centered on the Sun for use in mapping the local Galaxy. The data includes three columns for each star: parallax, heliocentric longitude, and heliocentric latitude. Data can be converted to Galactocentric Rectangular Coordinate (X, Y, Z) or Galactocentric Cylindrical Coordinate (R, Phi, Z). PLEASE NOTE: There are many incorrectly measured parallaxes -- all negative parallaxes must be removed.

Columns:

• parallax (units: mas) - Parallactic angle (used for determining the distance to a star).
• longitude (units: degrees) - The heliocentric longitude, with zero degrees corresponding to the direction of the Galactic Center.
• latitude (units: degrees) - The heliocentric latitude, with +90 degrees corresponding to the North Galactic Pole and 0 degrees corresponding to the Galaxy's mid-plane.

Query from Gaia Database:

SELECT gaia_source.parallax, gaia_source.l, gaia_source.b

FROM gaiadr3.gaia_source 

WHERE 

gaia_source.random_index < 5000000 AND

gaia_source.phot_g_mean_mag BETWEEN 14 AND 18 AND

gaia_source.bp_rp BETWEEN 0.5 AND 2.5 AND

(1.0 / gaia_source.parallax) * COS(RADIANS(gaia_source.b)) < 0.250

Note the final condition in the query limits the selection of stars to those within 250 parsecs (in-plane distance) of the Sun. In other words, we are examining the stars in a cylinder of radius 250 parsecs centered on the Sun, punching perpendicularly through the Milky Way disk.

License:

The Gaia Data is under the following license: Open Source With Attribution to ESA/Gaia/DPAC, reproduced here:

"The Gaia data are open and free to use, provided credit is given to 'ESA/Gaia/DPAC'. In general, access to, and use of, ESA's Gaia Archive (hereafter called 'the website') constitutes acceptance of the following general terms and conditions. Neither ESA nor any other party involved in creating, producing, or delivering the website shall be liable for any direct, incidental, consequential, indirect, or punitive damages arising out of user access to, or use of, the website. The website does not guarantee the accuracy of information provided by external sources and accepts no responsibility or liability for any consequences arising from the use of such data."

All of my course materials are free to use with attribution as well.

Maps are crucial for conveying the character and atmosphere of places. While maps that evoke a sense of place are increasingly recognized as important, existing methods often rely on subjective design choices, which can be inefficient and lack scalability. This paper proposes a method for generating place-aware colored maps using crowdsourced images to realize mapping with a sense of place. Feature colors of place extracted from these images are used to create colored maps that balance legibility, harmony, and imageability. To evaluate the method’s effectiveness, we conducted two comparative experiments: one comparing place-aware colored maps with Google Maps, and another with an aesthetically rich baseline—the “Hopper” style from Snazzy Maps. Evaluation was based on participants’ performance in map-reading tasks and their perceived sense of place. Results show that our colored maps perform similarly to Google Maps in map-reading tasks but significantly outperform both baselines in evoking a stronger sense of place. This indicates that place-aware colored maps effectively evoke a sense of place while maintaining basic map functionality. This research introduces a framework for mapping with a sense of place, offering a new approach to color generation that enhances user engagement through intuitive connections to place characteristics.

huggingface/map-test dataset hosted on Hugging Face and contributed by the HF Datasets community

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

Lanelet2 maps for the nuScenes dataset, which enable the usage of diverse map-based anchor paths and spatial semantic information. For details see our paper and project page. We also provide a pip package to facilitate the usage.

The maps were generated automatically and subsequently manually refined.

If you use this resource for scientific research, please consider citing

@InProceedings{naumannHertleinLanelet2NuScenes2023, author = {Naumann, Alexander and Hertlein, Felix and Grimm, Daniel and Zipfl, Maximilian and Thoma, Steffen and Rettinger, Achim and Halilaj, Lavdim and Luettin, Juergen and Schmid, Stefan and Caesar, Holger}, title = {Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-Based Anchor Paths}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3247-3256} }

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

This dataset contains 82 annotated map samples from diverse historical city maps of Jerusalem and Paris, suitable for map text detection, recognition, and sequencing.

Organization of the data

The data in maptext_format.json is organized in the same way as in the General Data from the David Rumsey Collection from ICDAR 2024 Competition on Historical Map Text Detection, Recognition, and Linking [1].

The data is structured by image, and list of sequences (groups). The boolean attributes illegible and truncated are used to provide additional insight on the data quality.

Our interpretation of the truncated and illegible tags is the following:

• truncated refers to the case where part of a word is located outside the image crop, and is thus missing. In that case, the transcription stops at the image border, focusing only on the visible part of the word
• illegible is a subjective indication of (un)certainty in the transcription provided. Whenever possible, a best guess transcription is provided. Otherwise, the illegible letters are filled with blank spaces

The text corresponds to the diplomatic transcription, i.e. as it appears on the document. Text are transcribes with all latin characters, with cases, diacritics (e.g. ö, ḡ) and diagraphs (e.g. Œ).

Each word polygon consists of an even number of vertices arranged in clockwise order starting from the initial point to the top left. The first n/2 vertices represent the upper boundary line following the reading direction, while the second half represents the lower boundary line in the reverse direction. Here is an illustration:

Example structure

[
{
"image": "map_image_1.jpg",

# Here groups are what we call sequences.
"groups": [
{
"vertices": [[x1, y1], [x2, y2], ...],
"text": "Champs",
"illegible": "false",
"truncated": "false"
},
{
"vertices": [[x1, y1], [x2, y2], ...],
"text": "Elysées",
"illegible": "false",
"truncated": "false"
}
]
}
]


The file pandas_format.pkl contains the same data. It is only provided for convenience.

Image documents

The maps of Paris were taken from the Historical City Maps Semantic Segmentation Dataset [2]. The original documents were digitized by the Bibliothèque nationale de France (BnF), and the Bibliothèque Historique de la Ville de Paris (BHVP).

The maps of Jerusalem were curated from the collections of the National Library of Israel (NLI), and Wikimedia Commons.

Descriptive statistics

Number of words: 7528
Number of single-word sequences: 1757
Number of multi-word sequences: 1969
Statistics of multi-word sequences length:
mean: 2.93 words
std: 1.25 words
min: 2.00 words
med: 3.00 words
max: 15.00 words

Future updates

The transcribed text, corresponds to the diplomatic transcription, suitable for text recognition tasks. In future updates, we hope to complement it with an additional normalization attribute, which could extend abbreviations (e.g. "bvd." => "boulevard") and normalize transcriptions (e.g. "QVARTER" => "QUARTER").

Use and Citation

For any mention of this dataset, please cite :

@misc{paris_jerusalem_dataset_2025,
author = {Dai, Tianhao and Johnson, Kaede and Petitpierre, R{\'{e}}mi and Vaienti, Beatrice and di Lenardo, Isabella},
title = {{Paris and Jerusalem City Maps Text Dataset}},
year = {2025},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.14982662}}

@article{recognizing_sequencing_2025,
author = {Zou, Mengjie and Dai, Tianhao and Petitpierre, R{\'{e}}mi and Vaienti, Beatrice and di Lenardo, Isabella},
title = {{Recognizing and Sequencing Multi-word Texts in Maps Using an Attentive Pointer}},
year = {2025}}

Corresponding author

Rémi PETITPIERRE - remi.petitpierre@epfl.ch - ORCID - Github - Scholar - ResearchGate

Work ethics

The data were annotated by two master's students from EPFL, Switzerland. The students were paid for their work using public funding, and were offered the possibility to be associated with the publication of the data.

License

This project is licensed under the CC BY 4.0 License.

Liability

We do not assume any liability for the use of this dataset.

References

1. Li Z., Lin Y., Chiang Y.-Y., Weinman J., Tual S., Chazalon J., Perret J., Duménieu B., Abadie N. (2024). ICDAR 2024 competition on historical map text detection, recognition, and linking. In Document Analysis and Recognition - ICDAR 2024: 18th International Conference, Athens, Greece, August 30–September 4, 2024, Proceedings, Part VI. Springer-Verlag, Berlin, Heidelberg, 363–380. https://doi.org/10.1007/978-3-031-70552-6_22
2. Petitpierre, R. (2021). Historical City Maps Semantic Segmentation Dataset. V1.0. https://doi.org/10.5281/zenodo.5513639

The Digital Geologic Map of the U.S. Geological Survey Mapping in the Western Portion of Amistad National Recreation Area, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Eddie Collins, Amanda Masterson and Tom Tremblay (Texas Bureau of Economic Geology); Rick Page (U.S. Geological Survey); Gilbert Anaya (International Boundary and Water Commission). Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (wpam_metadata.txt; available at http://nrdata.nps.gov/amis/nrdata/geology/gis/wpam_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (wpam_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 14N. The data is within the area of interest of Amistad National Recreation Area.

Division into map sheets of medium scale base maps (KZM 500) allows localization of map sheets of medium scale base maps using graphically suppressed content of the Map of the Czech Republic 1: 500,000. Map layout consists of the system of neat lines which indicates the relative position and identification of map sheets of Base maps 1:200,000, 1:100,000, 1:50,000, 1:25,000, 1:10,000. In addition map lettering includes standard geographical names, spot heights (altitude), numeric designation of map sheets of base maps at scales 1:25,000 to 1:200,000 in map layout, name and scale of map sheets, tirage data, data of graphic scale, text part of map legend and frame data (geographical coordinates). Map legend includes map layout of Base map 1:10,000, map layout of Base maps 1:50,000 and 1:25,000, limitation and examples of numeric designation of base maps at scales 1:10,000 to 1:200,000 and delimitation of Base map 1:10,000 map sheets. The subjects of topographic content KZM 500, with the exception of national administrative boundaries, are shown also on adjacent parts of the neighboring countries territory. In the overview of map layout neat lines are only in the neighboring countries territory of the map sheets which contain the Czech Republic territory.

The Community Map (World Edition) web map provides a customized world basemap that is uniquely symbolized and optimized to display special areas of interest (AOIs) that have been created and edited by Community Maps contributors. These special areas of interest include landscaping features such as grass, trees, and sports amenities like tennis courts, football and baseball field lines, and more. This basemap, included in the ArcGIS Living Atlas of the World, uses the Community vector tile layer. The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the layer items referenced in this map.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training, validation, and evaluation data from the map feature extraction challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

Map Data Platform Market Outlook

According to our latest research, the map data platform market size globally stood at USD 6.9 billion in 2024, reflecting robust expansion across diverse industry verticals. The market is experiencing a strong growth trajectory with a CAGR of 15.2% from 2025 to 2033. By the end of 2033, the global map data platform market is forecasted to reach USD 22.5 billion. This impressive growth is primarily driven by the surging demand for real-time geospatial analytics, rapid advancements in location-based services, and widespread integration of mapping technologies across sectors such as automotive, transportation, and retail.

The primary growth factor propelling the map data platform market is the increasing adoption of connected and autonomous vehicles. Automotive manufacturers are leveraging sophisticated map data platforms to enhance navigation, safety, and driver assistance features. The integration of high-definition mapping, real-time traffic updates, and advanced geocoding is critical for enabling autonomous driving and smart mobility solutions. This surge in automotive applications is further complemented by the proliferation of Internet of Things (IoT) devices, which rely on accurate geospatial data for asset tracking, fleet management, and location-based analytics. As a result, the automotive and transportation sectors are becoming significant contributors to the overall market growth, driving innovation and investment in map data platforms.

Another notable driver is the rapid expansion of location-based services (LBS) across multiple industries. Retailers, logistics companies, and government agencies are increasingly utilizing map data platforms to optimize operations, personalize customer experiences, and enhance decision-making processes. The widespread use of smartphones, coupled with advancements in mobile mapping technologies, has led to a surge in demand for real-time navigation, geofencing, and location-aware marketing. These trends are pushing platform providers to continuously innovate, offering scalable, cloud-based solutions that can handle vast volumes of geospatial data and deliver actionable insights to end-users. The convergence of artificial intelligence and big data analytics with mapping technologies is further amplifying the value proposition of map data platforms.

The evolution of smart cities and infrastructure development projects worldwide is also fueling the growth of the map data platform market. Governments and urban planners are increasingly relying on geospatial intelligence to manage resources, monitor public utilities, and enhance citizen services. The integration of mapping and visualization tools enables real-time monitoring of traffic, utilities, and public safety, supporting data-driven urban planning and sustainable development initiatives. As cities continue to invest in digital transformation, the demand for robust, scalable, and secure map data platforms is expected to accelerate, creating new opportunities for market players and stakeholders.

From a regional perspective, North America currently dominates the map data platform market, driven by the presence of leading technology companies, early adoption of advanced mapping solutions, and significant investments in autonomous vehicles and smart infrastructure. Europe follows closely, with strong growth in automotive and transportation sectors, while Asia Pacific is emerging as a high-growth region due to rapid urbanization, increasing smartphone penetration, and government initiatives supporting smart city projects. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by digital transformation efforts in retail, logistics, and public sector applications.

Component Analysis

The map data platform market by component is segmented into platform and services, each playing a distinct role in the ecosystem. The platform segment encompasses core mapping engines, geospatial data repositories, and APIs that enable developers and enterprises to integrate mapping functionalities into their applications. The rising demand for customizable and scalable platforms has led to significant investments in cloud-based mapping solutions, which offer high performance, real-time updates, and seamless integration with third-party systems. Platform providers are focusing on enhancing user experience through intuitive interfaces, advanced analytics,

Map Direct focus to show Submerged Lands Environmental Resources Coordination (SLERC) 404 Program information. Please refer to https://floridadep.gov/water/submerged-lands-environmental-resources-coordination for more information. Originally created 10/27/20 in Map Direct. Please contact GIS.Librarian@floridadep.gov for more information.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

For many people data is seen as abstract information. It is therefore valuable to use Matrixian Map, an interactive map that shows an enormous amount of data in one figure. It helps to make complex analyzes understandable, to see new opportunities and to make data-driven decisions.

With our large amount of consumer, real estate, mobility and logistics data we can design very extensive maps. Whether it concerns a map that shows your (potential) customers, shows on which roofs solar panels can be placed or indicates when shopping areas can be supplied, with our knowledge of households, companies and objects, almost anything is possible!

Map

## Overview

Map is a dataset for object detection tasks - it contains Objects Traffic annotations for 513 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Map Accuracy Monitoring Market Outlook

According to our latest research, the global map accuracy monitoring market size reached USD 1.37 billion in 2024, reflecting the growing importance of spatial data validation across diverse industries. The market is expected to expand at a compound annual growth rate (CAGR) of 11.2% from 2025 to 2033, ultimately attaining a value of USD 3.61 billion by 2033. This robust growth is primarily attributed to the increasing demand for reliable geospatial information, which is crucial for applications ranging from urban planning and transportation to agriculture and defense. As per our latest research, rapid technological advancements and the proliferation of location-based services are catalyzing the adoption of map accuracy monitoring solutions worldwide.

One of the primary growth factors propelling the map accuracy monitoring market is the heightened reliance on geographic information systems (GIS) and real-time mapping technologies across both public and private sectors. Modern infrastructure projects, smart city initiatives, and disaster management efforts are increasingly dependent on highly accurate and up-to-date geospatial data. The integration of advanced sensors, satellite imagery, and artificial intelligence has significantly improved the precision of mapping solutions, thereby driving the need for continuous map accuracy monitoring. Moreover, the rise of autonomous vehicles and drone-based surveying has further intensified the demand for precise and reliable map data, making accuracy monitoring an indispensable component of the geospatial ecosystem.

Another significant driver for the market is the growing regulatory emphasis on data integrity and compliance. Governments and regulatory bodies worldwide are mandating stringent standards for geospatial data, particularly in sectors such as defense, urban development, and environmental monitoring. This regulatory push compels organizations to invest in map accuracy monitoring tools and services to ensure compliance, minimize legal risks, and enhance operational efficiency. Additionally, the increasing frequency of natural disasters and the need for rapid response have underscored the importance of accurate mapping for emergency services and resource allocation. These factors collectively contribute to the sustained growth of the map accuracy monitoring market.

Technological innovation is also playing a pivotal role in shaping the market landscape. The advent of cloud-based platforms and scalable software solutions has democratized access to advanced map accuracy monitoring tools, enabling organizations of all sizes to benefit from real-time validation and correction mechanisms. Furthermore, the integration of machine learning algorithms and big data analytics allows for the automated detection of discrepancies and anomalies in map data, significantly reducing manual intervention and operational costs. As organizations increasingly recognize the strategic value of accurate geospatial information, investment in map accuracy monitoring solutions is expected to rise steadily over the forecast period.

From a regional perspective, North America currently leads the global map accuracy monitoring market, driven by substantial investments in smart infrastructure, defense modernization, and cutting-edge research initiatives. The presence of major technology companies and robust government support for geospatial innovation further bolster market growth in the region. Meanwhile, Asia Pacific is emerging as a high-growth market, propelled by rapid urbanization, expanding transportation networks, and the proliferation of digital mapping applications in countries such as China, India, and Japan. Europe also maintains a strong market presence, particularly in the domains of environmental monitoring and land management. Overall, the global map accuracy monitoring market exhibits a dynamic and geographically diverse growth trajectory.

Component Analysis

The map accuracy monitoring market is segmented by component into software, hardware, and services, each playing a unique and significant role in the ecosystem. Software solutions constitute the backbone of map accuracy monitoring, offering a range of functionalities from automated error detection and correction to advanced analytics and visualization. These platforms leverage cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing to deliver

Worcester Atlas is an interactive map viewer developed by the City of Worcester that gives the public access to city map layers and data, including property-specific assessor data.Users can search for property data by address, street, owner, or property ID, turn on/off map layers, get more information about certain layers in map popups, print maps, and more.More information: Visit the Introducing Worcester Atlas data story to get to know more about the City's map viewer.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

This layer will currently only work in map viewer classic. To add this layer to map viewer add a "Tile Layer" using this URL:https://maps.georeferencer.com/georeferences/6054ea4d-e58a-5f92-8ded-692e61946540/2019-04-16T08:58:10.159895Z/map/{z}/{x}/{y}.png?key=qHPmpZMW7Jh9puMskU5b