A systematic mapping review was conducted with the aim of providing an overall description of how linked data research has been used in UK decision-making relating to early life health; exploring the factors affecting the use of linked data as evidence in these decisions; and identifying where evidence gaps to inform further research.This mapping review forms part of a PhD project being undertaken by Hollie Henderson at the University of York, which aims to understand how linked data can be used as a local health intelligence tool for child and maternal health. This project is funded by the White Rose Consortium and is part of the National Institute for Health Research (NIHR) Yorkshire and Humber Applied Research Collaboration (YHARC).This document presents the Systematic Map that is associated with this mapping review.

map.social is a fun and engaging map-based outreach platform that allows users to individually or collectively create maps in a common map gallery. map.social allows residents, constituents, community stakeholders, and others to provide map referenced comments – a way for anyone to create a map of "their" community in a gallery that can be viewed by fellow community members. Individual maps can be collectively analyzed or brought into GIS for deeper analysis.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

The cloud-based mapping service market is experiencing robust growth, driven by the increasing adoption of location-based services across various sectors. The convergence of advanced technologies like AI, IoT, and big data analytics is fueling demand for sophisticated mapping solutions capable of handling vast datasets and delivering real-time insights. Key application areas, such as connected ADAS (Advanced Driver-Assistance Systems) and highly automated driving, are significant contributors to market expansion, demanding high-precision, dynamic mapping capabilities. The shift towards cloud-based infrastructure offers scalability, cost-effectiveness, and accessibility advantages over traditional on-premise solutions, further accelerating market penetration. Different map types, including analytical, animated, collaborative, and online atlases, cater to diverse needs, creating a multifaceted market landscape. While data security and privacy concerns represent potential restraints, the market is poised for sustained growth due to continuous technological advancements and expanding application domains. We estimate the 2025 market size to be approximately $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is underpinned by continuous innovation in mapping technologies and the expanding adoption of location intelligence across various industries. The major players in this market, including ESRI, Pitney Bowes, and CARTO, are investing heavily in R&D to enhance their offerings and gain a competitive edge. Regional variations exist, with North America and Europe currently holding significant market share, primarily due to higher technological adoption rates and established infrastructure. However, Asia Pacific is anticipated to witness rapid growth in the coming years, driven by increasing urbanization and expanding digital economies. The competitive landscape is characterized by both established players and emerging innovative companies striving for market dominance. This competitive dynamic is driving innovation and pushing the boundaries of what's possible with cloud-based mapping services, further contributing to the market's overall growth trajectory.

Passengers are our Passion and Purpose!We Provide Reliable Public Transit ServicesCasper Transit is premier resource for public transportation service in Casper, Evansville, Mills, Bar Nunn and parts of Natrona County. We can get you to a doctor’s appointment, to the grocery store or hair salon by using ASSIST’s reliable bus system or stay with the dependable fixed routes by using LINK.CONTACT USContact us if you have any questions about our transportation services by visiting us on the web here or call (307) 235-8273, or (307) 235-8287, or visit us at 1715 E. 4th Street, Casper, WY 82601. We look forward to helping you get where you need to go, reliably!Follow this link for a Printable LINK Map & Schedule (To read PDF files, you need the Adobe Acrobat Reader 6.0 or higher. Click here to download it for free from Adobe's site.)The City of Casper's transit program operates without regard to race, color or national origin. If you have questions about the City's Civil Rights obligations, please contact the City of Casper at (307) 235-8255.

Reconstructing past landscapes from historical maps requires quantifying the accuracy and completeness of these sources. The accuracy and completeness of two historical maps of the same period covering the same area in Israel were examined: the 1:63,360 British Palestine Exploration Fund map (1871-1877) and the 1:100,000 French Levés en Galilée (LG) map (1870). These maps cover the mountainous area of the Galilee (northern Israel), a region with significant natural and topographical diversity, and a long history of human presence. Land-cover features from both maps, as well as the contours drawn on the LG map, were digitized. The overall correspondence between land-cover features shown on both maps was 59% and we found that the geo-referencing method employed (transformation type and source of control points) did not significantly affect these correspondence measures. Both maps show that in the 1870s, 35% of the Galilee was covered by Mediterranean maquis, with less than 8% of the area used for permanent agricultural cropland (e.g., plantations). This article presents how the reliability of the maps was assessed by using two spatial historical sources, and how land-cover classes that were mapped with lower certainty and completeness are identified. Some of the causes that led to observed differences between the maps, including mapping scale, time of year, and the interests of the surveyors, are also identified.

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

A complete list of live websites using the Convert Address To Google Maps Link technology, compiled through global website indexing conducted by WebTechSurvey.

MethodThis dataset includes a detailed example for using our method (described in paper linked to below) to digitize historical land-use maps in R.MapsWe also release all of the Swedish land-use maps that we digitized for this project. This includes the Economic Map of Sweden (Ekonomiska kartan) over Sweden's 15 southernmost counties (7069 25 km2 sheets), plus 11 sheets of the District Economic Map (Häradsekonomiska kartan - but see http://bolin.su.se/data/Cousins-2015 for more accurate manual digitization).SvenskaHär kan du ladda ner 7069 Ekonomiska kartblad som vi digitaliserade över södra Sverige. En kort beskrivning av metoden publicerades i tidningen Kart & Bildteknik (se länk nedan).--UpdatesVersion 2: The digitized Economic Maps have been resampled so that they are all at a 1m resolution. In the original version they were all very close to 1m but not exactly the same, which made mosaicking difficult. This should be easier now. We now also link to the published paper in Methods in Ecology and Evolution.For more information, please see the readme file. For help or collaboration, please contact alistair.auffret@natgeo.su.se. If you use the data here in your work or research, please cite the publication appropriately.

Abstract This article presents a model for information retrieval in linked open data using methods and complex network operations for ranking and selecting information, and concept maps for presenting the retrieved information to the user. The model shows the relationships between query terms that represent an informational need and presents them as concept maps. The underlying hypothesis is that the user’s relationship to the retrieved information occurs in the light of Brookes’ fundamental equation of information science. The cognitive structure of the cognoscente is a complex network that is modulated by the retrieved information which, in turn, is derived from a complex network. The final complex network is mapped into a resulting concept map enhanced by heuristics, such as the application of controlled vocabulary. The first study conducted, with qualitative characteristics and using an exploratory approach, was an information retrieval pilot test. It allowed the assessment of the algorithms used in the ranking and selection of the intermediate information networks and provided the framework for the implementation of a prototype. The prototype used a knowledge base of linked open data, derived from DBpedia, on which complex network analysis were carried out. The validation of the model presented relevant recall and precision when applied to a group of 17 users. The results are promising for the use of complex network operations and concept maps for information retrieval, especially linked data. Further research should observe the demand for more interactive actions and conduct experiments in other knowledge bases.

The High Definition (HD) Maps market is experiencing significant growth, driven by the burgeoning autonomous vehicle (AV) sector and the increasing demand for advanced driver-assistance systems (ADAS). The market, estimated at $5 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated market value of $25 billion by 2033. This expansion is fueled by several key factors. Firstly, the continuous development and deployment of autonomous vehicles necessitate highly accurate and detailed map data for safe and efficient navigation. Secondly, the increasing sophistication of ADAS features, such as lane-keeping assist and adaptive cruise control, rely heavily on precise HD map information. Thirdly, the rising adoption of connected car technologies further propels demand for HD maps, enabling real-time traffic updates, improved route planning, and enhanced in-car infotainment experiences. Major players like TomTom, Google, Here Technologies, and Baidu Apollo are actively investing in research and development to improve map accuracy, update frequency, and data integration capabilities, fostering fierce competition and driving innovation within the market. However, challenges remain. High initial investment costs for HD map creation and maintenance represent a significant barrier to entry for smaller companies. Furthermore, data security and privacy concerns surrounding the collection and use of location data need careful consideration and robust regulatory frameworks. The need for constant map updates to reflect dynamic road conditions and infrastructure changes presents an ongoing operational challenge. Despite these hurdles, the long-term outlook for the HD Maps market remains exceptionally positive, with continued technological advancements and increasing adoption across various sectors promising substantial future growth. The market is segmented geographically, with North America and Europe currently holding the largest market share, but rapidly expanding markets in Asia-Pacific are poised for significant future growth due to the rising adoption of connected and autonomous vehicles in those regions.

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

description: This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.; abstract: This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted sample MAP messages in J2735 standards from 3 intersections in Orem, UT.

Aggregation of generic tables describing strategic noise maps pursuant to Article 3-II-1°-b of the Decree of 24 March 2006, for an infrastructure, type of infrastructure concerned: road, type of map b, areas affected by noise. Noise level zones describe a noise exposure situation based on a noise indicator or area affected by noise. They are used primarily for the preparation of strategic noise maps pursuant to Article R.572-5 of the Environmental Code. Noise exposure maps (or type a map) represent the isophone curves of 5 in 5 dB(A). The maps of the areas affected by noise (or “type b” maps) are defined by prefectural decrees. And limit value exceedance maps (type c map) represent for the mapping year the areas where the Lden and Ln limit values are exceeded (the Lden index (equivalent 24h sound level) and the Ln index (noise level over a night period from 22h to 6h)). Data source by infrastructure: CEREMA, ASF

Aggregation of generic tables describing strategic noise maps pursuant to Article 3-II-1°-b of the Decree of 24 March 2006, for an infrastructure, type of infrastructure concerned: rail, type of card c and Lden index. Noise level zones describe a noise exposure situation based on a noise indicator or area affected by noise. They are used primarily for the preparation of strategic noise maps pursuant to Article R.572-5 of the Environmental Code. Noise exposure maps (or type a map) represent the isophone curves of 5 in 5 dB(A). The maps of the areas affected by noise (or “type b” maps) are defined by prefectural decrees. And limit value exceedance maps (type c map) represent for the mapping year the areas where the Lden and Ln limit values are exceeded (the Lden index (equivalent 24h sound level) and the Ln index (noise level over a night period from 22h to 6h)). Data source by infrastructure: CEREMA, ASF

The HD Live Map market is experiencing robust growth, projected to reach a market size of $1279 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.8% from 2025 to 2033. This expansion is fueled by several key market drivers, including the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies in both commercial and military applications. The rising demand for precise and real-time location data for improved navigation, safety, and traffic management further contributes to this growth. Technological advancements, such as the development of high-resolution sensor technologies and improved data processing capabilities, are enhancing the accuracy and reliability of HD Live Maps, making them an indispensable component of next-generation vehicle systems. The market is segmented by crowdsourcing and centralized models, reflecting the varied approaches to data acquisition and map creation. Furthermore, application-based segmentation highlights the significant roles of commercial and military sectors, with the former encompassing automotive, logistics, and ride-sharing applications, while the latter emphasizes defense and security operations. Leading players such as TomTom, Google, Alibaba (AutoNavi), and Baidu are actively investing in R&D and strategic partnerships to consolidate their market positions. The competitive landscape is dynamic, with established players and emerging technology firms competing to deliver superior map data and services. The geographical distribution of the HD Live Map market is diverse, with North America and Asia Pacific expected to dominate due to significant investments in autonomous vehicle technology and robust infrastructure development. Europe is also a significant market, driven by strong government support for technological innovation and the growing adoption of connected car services. The market growth will be influenced by factors such as government regulations related to autonomous driving, the cost of data acquisition and processing, and the increasing integration of HD Live Maps into various smart city initiatives. The ongoing development of 5G networks and the rise of IoT devices are also expected to further stimulate market growth in the coming years. Continuous improvement in map accuracy and detail, coupled with wider industry adoption, will remain pivotal to the market's sustained expansion throughout the forecast period.

Aggregation of generic tables describing strategic noise maps pursuant to Article 3-II-1°-b of the Decree of 24 March 2006, for an infrastructure, type of infrastructure concerned: road, type of map b, areas affected by noise. Noise level zones describe a noise exposure situation based on a noise indicator or area affected by noise. They are used primarily for the preparation of strategic noise maps pursuant to Article R.572-5 of the Environmental Code. Noise exposure maps (or type a map) represent the isophone curves of 5 in 5 dB(A). The maps of the areas affected by noise (or “type b” maps) are defined by prefectural decrees. And limit value exceedance maps (type c map) represent for the mapping year the areas where the Lden and Ln limit values are exceeded (the Lden index (equivalent 24h sound level) and the Ln index (noise level over a night period from 22h to 6h)). Data source by infrastructure: CEREMA, ASF

The Perry-Castañeda Library Map Collection (PCL 1.306) is a general collection of more than 250,000 maps covering all areas of the world. Many of the maps are included in the University of Texas …Show full descriptionThe Perry-Castañeda Library Map Collection (PCL 1.306) is a general collection of more than 250,000 maps covering all areas of the world. Many of the maps are included in the University of Texas Library Catalog. More than 11,000 map images from the collection are also available online via this link. Maps were produced by the U.S. Central Intelligence Agency, unless otherwise indicated. Maps dated 1976 were taken from The Indian Ocean Atlas, published by the Central Intelligence Agency.