Dataset

This dataset was created by K Scott Mader

Contents

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 georeferencing 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.

SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.SDNist is available via pip install: pip install sdnist==1.2.8 for Python >=3.6 or on the USNIST/Github. The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.

Files and metadata associated with the EMDataBank/Unified Data Resource for 3DEM 2015/2016 Map Challenge hosted at challenges.emdatabank.org are deposited.

All members of the Scientific Community--at all levels of experience--were invited to participate as Challengers, and/or as Assessors.

Seven benchmark raw image datasets were selected for the challenge. Six are selected from recently described single particle structure determinations with image data collected as multi-frame movies; one is based on simulated (in silico) images. All of the raw image datasets are archived at pdbe.org/empiar.

27 Challengers created 66 single particle reconstructions from the targets, and then uploaded their results with associated details. 15 of the reconstructions were calculated using the SDSC Gordon supercomputer.

This map challenge was one of two community-wide challenges sponsored by EMDataBank in 2015/2016 to critically evaluate 3DEM methods that are coming into use, with the ultimate goal of developing validation criteria associated with every 3DEM map and map-derived model.

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The mobile map market share is expected to increase by USD 6.73 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 18.41%. This mobile map market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. The mobile map market report also offers information on several market vendors, including Alibaba Group Holding Ltd., Alphabet Inc., Apple Inc., CE Info. Pvt. Ltd., Environmental Systems Research Institute Inc., HERE Global BV, Microsoft Corp., NavInfo Co. Ltd., TomTom International BV, and Verizon Communications Inc. among others. Furthermore, this report extensively covers mobile map market segmentation by application (outdoor mobile map and indoor mobile map), end-user (automotive navigation, mobile and internet, and public sector and enterprise), and geography (APAC, North America, Europe, South America, and MEA).

What will the Mobile Map Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Mobile Map Market Size for the Forecast Period and Other Important Statistics

Mobile Map Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The increasing adoption of technologically advanced mobile devices is notably driving the mobile map market growth, although factors such as threat from open-source platform may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the mobile map industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Mobile Map Market Driver

The increasing adoption of technologically advanced mobile devices is one of the primary factors driving the mobile map market growth. The growing penetration of advanced mobile devices has increased the use of location-based services (LBS). To support this, mobile device manufacturers are introducing new devices that can integrate location-based applications such as GPS-enabled applications. In addition, individuals rely on such devices to obtain information such as traffic updates, directions to nearby locations, and real-time information such as weather forecasts. All these GPS-based applications are built on digital maps. Furthermore, the growth in connected devices will drive the demand for mobile maps globally to enable seamless navigation.

Key Mobile Map Market Trend

The development of indigenous mapping systems is one of the major mobile map market trends. This trend is growing significantly in Brazil, Russia, China, and India. Governments are encouraging regional mobile map makers to develop mobile map solutions that are country-specific. This trend is further supported by advanced mapping technology, which can develop accurate 3D digital maps.

Key Mobile Map Market Challenge

The growing popularity of open-source solutions has an adverse effect on the net sales of commercial mobile map solutions. The inflated cost of mobile map solutions has increased the demand for open-source mobile map applications in the market, especially in emerging countries such as China and India. These nations consist of many SMEs that require mobile map solutions but do not have sufficient capital to invest in customized mobile map technology. Therefore, open-source mobile map solutions have become a preferred choice among them. Many automobile companies also prefer open-source mobile map solutions in their vehicles. Large companies, as a part of the cost reduction, now prefer using open-source mobile map solutions compared with commercial mobile map solutions.

This mobile map market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Mobile Map Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alibaba Group Holding Ltd.
Alphabet Inc.
Apple Inc.
CE Info. Pvt. Ltd.
Environmental Systems Research Institute Inc.
HERE Global BV
Microsoft Corp.
NavInfo Co. Ltd.
TomTom International BV
Verizon Communications Inc.

This statistical study of the mobile map market encompasses successful business strategies deployed by the key vendors. The mobile mapping market is fragmented and the vendors are deploying growth strategies such as M&A activities to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments

Context

Differential Privacy Temporal Map Challenge - Sprint 2 Data

Content

The dataset includes survey data, including demographic and financial features, representing a subset of IPUMS American Community Survey data for Ohio and Illinois from 2012-2018. The data includes a large feature set of quantitative survey variables along with simulated individuals (with a sequence of records across years), time segments (years), and map segments (PUMA).### Context

Acknowledgements

The data was provided by NIST PSCR for Sprint 2 of the Differential Privacy Temporal Map Challenge.

Inspiration

The data can be used to test solutions in the differential privacy field.

SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.SDNist is available via pip install: pip install sdnist==1.2.8 for Python >=3.6 or on the USNIST/Github. The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.

Context

Differential Privacy Temporal Map Challenge - Sprint 3 Data

Content

The dataset includes quantitative and categorical information about taxi trips in Chicago, including time, distance, location, payment, and service provider. The data includes several features along with time segments (trip_day_of_week and trip_hour_of_day), map segments (pickup_community_area and dropoff_community_area), and simulated individuals (taxi_id).

Acknowledgements

The data was provided by NIST PSCR for Sprint 3 of the Differential Privacy Temporal Map Challenge.

Inspiration

The data can be used to test solutions in the differential privacy field.

This is the full dataset of the 2021 Cryo-EM Map-based Model Ligand Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: (1) E. coli beta-galactosidase with inhibitor, (2) SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and (3) SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details.

File Descriptions:

• 2021-EMDataResource-Ligand-Challenge-web.pdf: Archive of challenges website content describing the challenge including News, Overview, Goals, Model Evaluation, Timeline, Targets, How to Participate, Q&A, Advisory Committee, Ligand Images
• T010X.zip: Submitted model files for each target (mmCIF and PDB formats) used for analyses
• S1_Ligand_Challenge_Statistics_Submission_Form.pdf: Overall model statistics, model submission form guide
• S2_submission_metadata.xlsx: Complete set of metadata collected for each submitted model
• S3_ligandchallengescores.xlsx: Compiled set of scores for this challenge from the model-compare site.
• WrapUp_MeetingAgendaLigandChallenge.pdf: Agenda of the July 26-28 2021 wrap up meeting
• zoom_discussion_mc_results.mp4: July 14 Model-Compare site analysis live demo recording

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

This data contains the scores from the Residential Displacement Risk Map, created by the Mayor’s Office of Housing (MOH) and released in March of 2025. The Residential Displacement Risk Map is Boston’s first interactive map measuring current displacement pressures and levels of residential displacement risk across Boston. The map aims to increase understanding of this challenge, and will be updated every couple of years to keep track of changing patterns.

This map is part of Boston’s first ever Anti-Displacement Action Plan. The Action Plan responds to residential, small business, and cultural displacement with new tools to fill gaps in Boston’s existing anti-displacement toolkit. It will also better position the City to target resources to people, places, and spaces at greatest risk of displacement, and it includes recommendations for how to use this map in planning, policy, and development decision making.

The Residential Displacement Risk Map can also be used to raise awareness of displacement and housing instability challenges and provide a data-driven understanding of displacement risk. It is meant to be used by the City, residents, community organizations, academics, housing advocates, and more.

The Residential Displacement Risk Map measures community-level displacement, meaning how likely it is for high numbers of households to be displaced from an area, changing its fundamental demographic makeup. The Residential Displacement Risk Map does not measure household- or individual-level displacement risk, or how likely it is for any one household or individual to be displaced. Those who live in a high-risk area will not necessarily be displaced. The map only paints a general picture of an area’s sensitivity to displacement pressures. A higher score indicates a higher risk of displacement.

The Residential Displacement Risk Map measures direct displacement (when residents are forced to move from their homes, such as in an eviction or a foreclosure) and estimates economic displacement (when current residents of an area can no longer afford to live there). The map uses direct displacement as a guidepost for predicting where economic displacement is likely to occur, based on a variety of characteristics that are associated with direct displacement. If an area has high direct displacement (evictions and foreclosures), then it is likely to also have high economic displacement. More detail on how the Residential Displacement Risk Map measures risk can be found in the technical documentation linked below.

The Displacement Risk Map can be directly accessed here: https://experience.arcgis.com/experience/177e64a85f4041d2b4655d7cd1991c56/

Learn more about the City’s Anti-Displacement Action Plan here: https://www.boston.gov/departments/planning-advisory-council/anti-displacement-action-plan#:~:text=It%20lays%20out%20priority%20policies,and%20preserving%20existing%20affordable%20housing

Technical documentation for the map can be accessed here: https://docs.google.com/document/d/1ctv0S67Rx5GA46GbY_Glo_y-JYoQRCMS336yPDw_18o/edit?usp=sharing

Maps are increasingly read on mobile devices. Mobile maps necessitate specific design considerations to improve readability and user experience. Little research has focused on how to design mobile thematic maps, in contrast to reference maps. Data journalism represents a common way that the public encounters mobile thematic maps. This paper characterizes the design techniques and challenges associated with mobile thematic cartography in the context of data journalism. Through interviews with 18 expert news cartographers, I show that teams of data journalists are increasingly aware of mobile users, but face numerous constraints when designing for these users. They face time constraints, the need to design for both desktop and mobile, and must reach vast general audiences, meaning they often practice simultaneous design over mobile-first design. News cartographers have also reduced their use of interactivity, which reduces complexity related to designing for both desktop and mobile. This work shows that news cartographers solve mobile thematic map design challenges through iterative design processes that draw from years of expertise, not a strict set of guidelines. News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readersMany news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the otherNews cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possibleNews maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing News cartographers currently design mobile thematic maps based on generalized best practices, but are uncertain what choices do and do not work for their readers Many news cartographers design maps simultaneously for desktop and mobile, rather than prioritizing one over the other News cartographers are decreasing their use of interactive maps, given that they expect news readers want to consume information as fast as possible News maps are produced under time constraints that can be limiting on creativity and novelty, and without time for user testing

Conservation planning in the Great Plains often depends on understanding the degree of fragmentation of the various types of grasslands and savannas that historically occurred in this region. To define ecological subregions of the Great Plains, we used a revised version of Kuchler’s (1964) map of the potential natural vegetation of the United States. The map was digitized from the 1979 physiographic regions map produced by the Bureau of Land Management, which added 10 physiognomic types. All analyses are based on data sources specific to the United States; hence, we only analyze the portion of the Great Plains occurring in the United States.We sought to quantify the current amount of rangeland in the US Great Plains converted due to 1) woody plant encroachment; 2) urban, exurban, and other forms of development (e.g., energy infrastructure); and 3) cultivation of cropland. At the time of this analysis, the most contemporary measure of land cover across the United States was the 2011 NLCD (Homer et al. 2015). One limitation of the NLCD is that some grasslands with high rates of productivity, such as herbaceous wetlands or grasslands along riparian zones, are misclassified as cropland. A second limitation is the inability to capture cropland conversion occurring after 2011 (Lark et al. 2015). Beginning in 2009 (and retroactively for 2008), the US Department of Agriculture - NASS has annually produced a Cropland Data Layer (CDL) for the United States from satellite imagery, which maps individual crop types at a 30-m spatial resolution. We used the annual CDLs from 2011 to 2017 to map the distribution of cropland in the Great Plains. We merged this map with the 2011 NLCD to evaluate the degree of fragmentation of grasslands and savannas in the Great Plains as a result of conversion to urban land, cropland, or woodland. We produced two maps of fragmentation (best case and worst case scenarios) that quantify this fragmentation at a 30 x 30 m pixel resolution across the US Great Plains, and make them available for download here. Resources in this dataset: Resource title: Data Dictionary for Figure 2 derived land cover of the US portion of the North American Great Plains File name: Figure2_Key for landcover classes.csv Resource title: Figure 1. Potential natural vegetation of US portion of the North American Great Plains, adapted from Kuchler (1964). File name: Figure1_Kuchler_GPRangelands.zip Resource description: Extracted grassland, shrubland, savanna, and forest communities in the US Great Plains from the revised Kuchler natural vegetation map Resource title: Figure 2. Derived land cover of the US portion of the North American Great Plains. File name: Figure2_Key for landcover classes.zip Resource description: The fNLCD-CDL product estimates that 43.7% of the Great Plains still consists of grasslands and shrublands, with the remainder consisting of 40.6% cropland, 4.4% forests, 3.0% UGC, 3.0% developed open space, 2.9% improved pasture or hay fields, 1.2% developed land, 1.0% water, and 0.2% barren land, with important regional and subregional variation in the extent of rangeland loss to cropland, forests, and developed land. Resource title: Figure 3. Variation in the degree of fragmentation of Great Plains measured in terms of distance to cropland, forest, or developed lands. File name: Figure3_bestcase_disttofrag.zip Resource description: This map depicts a “best case” scenario in which 1) croplands are mapped based only on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017), 2) all grass-dominated cover types including hay fields and improved pasture are considered rangelands, and 3) developed open space (as defined by the National Land Cover Database) are assumed to not be a fragmenting land cover type. Resource title: Figure 4. Variation in the degree of fragmentation of Great Plains measured in terms of distances to cropland, forest, or developed lands. File name: Figure4_worstcase_disttofrag.zip Resource description: This map depicts a ‘worst case’ scenario in which 1) croplands are mapped based on the US Department of AgricultureNational Agricultural Statistics Service Cropland Data Layers (2011e2017) and the 2011 National Land Cover Database (NLCD), 2) hay fields and improved pasture are not included as rangelands, and 3) developed open space (as defined by NLCD) is included as a fragmenting land cover type.

This article examines the significance of the world map in video games for the interpretation of spatial situations. An example is the popular role-playing game The Witcher 3: Wild Hunt. Nowadays, most video games are characterized by the presence of a spatial aspect. The game world map is the most important navigational element of the game that the gamer can use. To this end, the authors decided to test the importance of the game world map in the context of analyzing different examples of spatial situations that appear in The Witcher 3: Wild Hunt by the respondents. Eye movement tracking was chosen as the research method. The analysis was conducted using statistical tests. Both gamers and non-gamers of The Witcher 3: Wild Hunt, gamers and non-gamers in general, and people who identified themselves as women or men participated in the survey. Each subject was shown 5 movies (1 introductory movie, 4 movies in the main part of the study) from the gameplay of the game, in which the game world map was opened. After each video, a question was asked about both the gameplay and the game world map. It was found that familiarity with The Witcher 3: Wild Hunt, frequency of playing video games and gender influenced the correctness and time of answering the questions asked. In addition, it was found that the game world map and gameplay segments do not cognitively burden the users. Differences in visual strategy were observed between the groups of test subjects. The authors emphasized the importance of conducting further research on video games in relation to the analysis of spatial situations.

Derek H. Alderman, Joshua F.J. Inwood, Ethan BottoneThe mapping behind the movement: On recovering the critical cartographies of the African American Freedom Struggle,Geoforum,Volume 120,2021,Pages 67-78,ISSN 0016-7185,https://doi.org/10.1016/j.geoforum.2021.01.022.(https://www.sciencedirect.com/science/article/pii/S0016718521000300)Abstract: Responding to recent work in critical cartographic studies and Black Geographies, the purpose of this paper is to offer a conceptual framework and a set of evocative cartographic engagements that can inform geography as it recovers the seldom discussed history of counter-mapping within the African American Freedom Struggle. Black resistant cartographies stretch what constitutes a map, the political work performed by maps, and the practices, spaces, and political-affective dimensions of mapping. We offer an extended illustration of the conventional and unconventional mapping behind USA anti-lynching campaigns of the late 19th and early 20th centuries, highlighting the knowledge production practices of the NAACP and the Tuskegee Institute’s Monroe Work, and the embodied counter-mapping of journalist/activist Ida B. Wells. Recognizing that civil rights struggles are long, always unfolding, and relationally tied over time and space, we link this look from the past to contemporary, ongoing resistant cartographical practices as scholars/activists continue to challenge racialized violence and advance transitional justice, including the noted memory-work of the Equal Justice Initiative. An understanding of African American traditions of counter-mapping is about more than simply inserting the Black experience into our dominant ideas about cartography or even resistant mapping. Black geographies has much to teach cartography and geographers about what people of color engaged in antiracist struggles define as geographic knowledge and mapping practices on their own terms—hopefully provoking a broader and more inclusive definition of the discipline itself.Keywords: African American; Anti-lynching; Black geographies; Civil rights; Counter-mapping; Critical cartography

This is the full dataset of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 Å) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several new metrics were introduced.

File Descriptions:

• 2019-EMDataResource-Challenge-web.pdf: Archive of News, Goals, Timeline, Targets, Modelling Instructions, Process, FAQ, Submission Instructions, Submission Summary Statistics source from the EMDR Challenges website
• correlation-images.tar.gz: Pairwise correlation tables for selected metric scores from the EMDR Model Compare website
• maps.tar.gz: The maps used for Fit-to-Map analyses in the Challenge
• models.tar.gz: The 63 models submitted by the modelling teams
• results.tar.gz: The output logs for all of the analysis methods
• Scores.xlsx: Scores for each model and analysis method, compiled into spreadsheet format
• targets.tar.gz: The reference models used in the analysis

Post submission correction to the web archive PDF document: The full list of EMDataResource members on the model committee is as follows: Cathy Lawson, Andriy Kryshtafovych, Greg Pintilie, Mike Schmid, Helen Berman, Wah Chiu.

Generally speaking, the stakes are people, property, activities, cultural or environmental heritage elements, threatened by a hazard and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This object class brings together all the issues that have been addressed in the RPP study. An issue is a dated object whose consideration depends on the purpose of the RPP and its vulnerability to the hazards studied. A PPR issue can therefore be considered (or not) depending on the type or types of hazard being addressed. These elements form the basis of knowledge of the land cover necessary for the development of the RPP, in or near the study area, at the time of the analysis of the issues. The data on issues represent a (figible and non-exhaustive) photograph of assets and individuals exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the issues are recalculated as necessary with up-to-date data sources.

description: Minnesota's original public land survey plat maps were created between 1848 and 1907 during the first government land survey of the state by the U.S. Surveyor General's Office. This collection of more than 3,600 maps includes later General Land Office (GLO) and Bureau of Land Management maps up through 2001. Scanned images of the maps are available in several digital formats and most have been georeferenced. The survey plat maps, and the accompanying survey field notes, serve as the fundamental legal records for real estate in Minnesota; all property titles and descriptions stem from them. They also are an essential resource for surveyors and provide a record of the state's physical geography prior to European settlement. Finally, they testify to many years of hard work by the surveying community, often under very challenging conditions. The deteriorating physical condition of the older maps (drawn on paper, linen, and other similar materials) and the need to provide wider public access to the maps, made handling the original records increasingly impractical. To meet this challenge, the Office of the Secretary of State (SOS), the State Archives of the Minnesota Historical Society (MHS), the Minnesota Department of Transportation (MnDOT), MnGeo (formerly the Land Management Information Center - LMIC) and the Minnesota Association of County Surveyors collaborated in a digitization project which produced high quality (800 dpi), 24-bit color images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes of data. Funding was provided by MnDOT. In 2010-11, most of the JPEG plat map images were georeferenced. The intent was to locate the plat images to coincide with statewide geographic data without appreciably altering (warping) the image. This increases the value of the images in mapping software where they can be used as a background layer.; abstract: Minnesota's original public land survey plat maps were created between 1848 and 1907 during the first government land survey of the state by the U.S. Surveyor General's Office. This collection of more than 3,600 maps includes later General Land Office (GLO) and Bureau of Land Management maps up through 2001. Scanned images of the maps are available in several digital formats and most have been georeferenced. The survey plat maps, and the accompanying survey field notes, serve as the fundamental legal records for real estate in Minnesota; all property titles and descriptions stem from them. They also are an essential resource for surveyors and provide a record of the state's physical geography prior to European settlement. Finally, they testify to many years of hard work by the surveying community, often under very challenging conditions. The deteriorating physical condition of the older maps (drawn on paper, linen, and other similar materials) and the need to provide wider public access to the maps, made handling the original records increasingly impractical. To meet this challenge, the Office of the Secretary of State (SOS), the State Archives of the Minnesota Historical Society (MHS), the Minnesota Department of Transportation (MnDOT), MnGeo (formerly the Land Management Information Center - LMIC) and the Minnesota Association of County Surveyors collaborated in a digitization project which produced high quality (800 dpi), 24-bit color images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes of data. Funding was provided by MnDOT. In 2010-11, most of the JPEG plat map images were georeferenced. The intent was to locate the plat images to coincide with statewide geographic data without appreciably altering (warping) the image. This increases the value of the images in mapping software where they can be used as a background layer.

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants. This dataset includes the fastq files provided to participants, the submitted variant callset as vcfs, and the benchmarking results, along with challenge submission metadata.

The global Digital HD Map market is experiencing robust growth, projected to reach $1558.9 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.4% from 2025 to 2033. This expansion is driven by the increasing demand for precise location data across various sectors. The automotive industry, particularly autonomous vehicles, is a major catalyst, relying heavily on highly detailed and accurate maps for navigation and safety features. Furthermore, the burgeoning use of augmented reality (AR) and virtual reality (VR) applications, coupled with the expanding smart city initiatives globally, fuels the market's growth trajectory. The rise of advanced driver-assistance systems (ADAS) and the integration of digital maps into connected car platforms also contribute significantly to this market's expansion. Competition within the market is fierce, with established players like Google, TomTom, and HERE Technologies competing alongside emerging innovative companies. The market segmentation by map type (2D HD Map, 3D HD Map) and application (Commercial Use, Military Use, Others) reflects the diverse range of applications and associated technological advancements shaping this dynamic landscape. Different regions contribute varying levels of market share, with North America and Asia-Pacific anticipated to lead due to significant technological advancements and higher adoption rates. The market's growth is not without its challenges. Data acquisition and maintenance costs remain a significant hurdle, especially for maintaining the accuracy and timeliness of high-resolution map data. Ensuring data security and privacy, particularly with the increased use of location data in various applications, presents another substantial challenge. Regulatory frameworks governing the use and collection of such data vary across different geographies, creating complexities for businesses operating internationally. Despite these challenges, the long-term prospects for the Digital HD Map market remain positive, driven by continuous technological innovations, increasing investment in autonomous driving technologies, and the expanding need for precise location intelligence across diverse industry verticals. The market is expected to see further consolidation through mergers and acquisitions as companies strive to enhance their capabilities and market share.