The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points). All of the Digital City Map (DCM) datasets are featured on the Streets App All previously released versions of this data are available at BYTES of the BIG APPLE- Archive Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters: Athens, Cairo, Jakarta, Moscow, Mumbai, Nairobi, Paris, Rio De Janeiro, Shanghai

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

The Nova Map (World Edition) web map provides a detailed world basemap featuring a dark background with glowing blue symbology and colors that are reminiscent of science-fiction shows, where one is looking at a map of the world on a 'head's up' device or a map that would be projected from a transparent glass wall. The map is designed with a grid pattern across the ocean and stripes or square stippled patterns for land use features visible at larger scales. Additional graphics in the oceans presents a futuristic user interface. The futuristic and less terrestrial feel theme continues with the geometric patterns, starburst city dot symbols, and cool color scheme. The fonts displayed are clean and squarish (san serif) with a futuristic, science-fiction, or high technology appearance.This basemap, included in the ArcGIS Living Atlas of the World, uses the Nova 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 tile layer referenced in this map.

Interactive Map Creation Tools Market Outlook

The global market size for Interactive Map Creation Tools was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% during the forecast period. The primary growth factors for this market include the increasing need for advanced geospatial data visualization, the rise of smart city initiatives, and the growing demand for real-time location-based services.

One of the key growth drivers is the increasing demand for geospatial analytics across various sectors such as urban planning, transportation, and environmental monitoring. As urbanization accelerates, city planners and government authorities are turning to interactive mapping tools to visualize complex data sets that help in making informed decisions. These tools assist in laying out city infrastructures, optimizing traffic routes, and planning emergency response strategies. The trend towards smart cities further amplifies the need for such sophisticated tools, which can handle dynamic and interactive data layers in real-time.

The transportation sector also finds significant utility in interactive map creation tools. With the surge in smart transportation projects globally, there is a mounting need to integrate real-time data into interactive maps for efficient route planning, traffic management, and logistics operations. Such tools not only aid in reducing congestion and travel times but also contribute to making transportation systems more sustainable. Additionally, interactive maps are becoming vital for managing fleets in logistics, enhancing the efficiency of delivery networks and reducing operational costs.

Environmental monitoring is another critical application area driving market growth. With increasing concerns about climate change and natural disasters, there is a heightened need for tools that can provide real-time environmental data. Interactive maps enable organizations to monitor various environmental parameters such as air quality, water levels, and wildlife movements effectively. These tools are instrumental in disaster management, helping authorities to visualize affected areas and coordinate relief operations efficiently.

Regionally, North America has been the dominant market for interactive map creation tools, driven by the high adoption of advanced technologies and significant investments in smart city projects. Europe follows closely, with countries like Germany and the UK leading the charge in urban planning and environmental monitoring initiatives. The Asia Pacific region is expected to witness the fastest growth, fueled by rapid urbanization and increasing investments in infrastructure development. Emerging economies in Latin America and the Middle East & Africa are also exploring these tools to address urbanization challenges and improve municipal services.

In addition to the regional growth dynamics, the emergence of Custom Digital Map Service is revolutionizing the way organizations approach geospatial data. These services offer tailor-made mapping solutions that cater to the unique needs of businesses and government agencies. By providing highly customizable maps, these services enable users to integrate specific data layers, adjust visual styles, and incorporate branding elements, thereby enhancing the utility and appeal of the maps. As the demand for personalized mapping solutions grows, Custom Digital Map Service is becoming a vital component in sectors such as urban planning, logistics, and tourism, where tailored insights can drive strategic decisions and improve operational efficiency.

Component Analysis

In the Interactive Map Creation Tools market, the component segment is divided into Software and Services. The Software segment comprises products such as GIS software, mapping platforms, and data visualization tools. This segment holds a significant share of the market, fueled by the rising need for sophisticated software solutions that can handle vast amounts of geospatial data. Advanced mapping software offers features like real-time data integration, multi-layer visualization, and high customization capabilities, making it an indispensable tool for various industries.

The increasing complexity

The MAPS dataset is one of the most used benchmark dataset for automatic music transcription. We propose here an updated version of the ground truth MIDI files, containing, on top of the original pitch, onset and offsets, additional annotations.

The annotations include:

• Tempo curve

• Time signature

• Durations of notes in fraction of a quarter note (some of them are approximate)

• Key signature (always written as the major relative)

• Sustain pedal activation

• Separate left and right hand staff

• Text annotations from the score (tempo indications, coda...).

If you use these annotations in a published research project, please cite:
Adrien Ycart and Emmanouil Benetos. “A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations” 19th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, September 2018, Paris, France.

More information is available at: http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

This dataset contains the Soil map of the Grand-Duchy of Luxembourg at scale of 1:100.000. It contains a classification of the complete territory in 29 soil mapping units (SMU) containing information on texture, stoniness, nature of coarse fragments, drainage and simplified pedogenetic classification. The dataset has been published in 1969. Description copied from catalog.inspire.geoportail.lu.

The digital topographic maps are generated from digital landscape and terrain models as well as the official real estate cadastre information system ALKIS and visualized according to the nationwide ATKIS signature catalogue. They are available in a maximum of 23 content levels (according to the technical regulations of the AdV) in three forms (individual levels, gray combination and color combination). The data are comprehensive and available in the uniform geodetic reference system and map projection for the state of Brandenburg. The raster data is divided into different levels according to cartographic content elements. They are delivered without page cuts as single-color individual levels (layers) and as colored combined editions in a uniform resolution. In addition, the data is offered in the standard sheet format (with map frame and legend) as a PDF and as a plotted map. They are available as web services, as raster data and as analogue map prints (plots). When using the data, the license conditions must be observed.

Abstract ======== The Mercury Dual Imaging System (MDIS) consists of two cameras, a Wide Angle Camera (WAC) and a Narrow Angle Camera (NAC), mounted on a common pivot platform. This dataset includes Map Projected High- Incidence Angle Basemap Illuminated from the West RDRs (HIWs) which comprise a global map of I/F measured by the NAC or WAC filter 7 (both centered near 750 nm) during the the Extended Mission at high incidence angles to accentuate subtle topography, photometrically normalized to a solar incidence angle (i) = 30 degrees, emission angle (e) = 0 degrees, and phase angle (g) = 30 degrees at a spatial sampling of 256 pixels per degree. The HIW data set is a companion to the Map Projected High-Incidence Angle Basemap Illuminated from the East RDR (HIE) data set. Together the two data sets are intended to detect and allow the mapping of subtle topography. They complement a Basemap Data Record (BDR) data set also composed of WAC filter 7 and NAC images acquired at moderate/high solar incidence angles centered near 68 degrees (changed to 74 degrees in the find end-of-mission data delivery), and an Low Incidence Angle (LOI) data set also composed of WAC filter 7 and NAC images acquired at lower incidence centered near 45 degrees, analogous to the geometry used for color imaging. The map is divided into 54 'tiles', each representing the NW, NE, SW, or SE quadrant of one of the 13 non-polar or one of the 2 polar quadrangles or 'Mercury charts' already defined by the USGS. Each tile also contains 5 backplanes: observation ID; BDR metric, a metric used to determine the stacking order of component images, modified for the higher incidence angle centered near 78 degrees; solar incidence angle; emission angle; and phase angle.

Data Mapping Software Market Outlook

The global data mapping software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This robust growth can be attributed to the increasing need for data integration and enhanced data management strategies across various industries.

One of the primary factors driving the growth of the data mapping software market is the escalating demand for data integration tools that can handle the ever-increasing volume and complexity of data. With businesses generating vast amounts of data from disparate sources, the need for effective data mapping solutions that enable seamless data integration and accurate data analysis has become indispensable. Consequently, organizations are increasingly investing in advanced data mapping software to streamline their data processes and gain actionable insights.

Moreover, the proliferation of big data and the advent of technologies such as Artificial Intelligence (AI) and Machine Learning (ML) have further amplified the need for robust data mapping solutions. These technologies require well-organized and accurately mapped data to function optimally and deliver valuable insights. As companies continue to adopt AI and ML for various applications, the demand for data mapping software is expected to surge, driving market growth over the forecast period.

Another significant growth factor is the growing emphasis on regulatory compliance and data governance. Organizations are under increasing pressure to comply with various data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Data mapping software plays a crucial role in helping businesses ensure compliance by providing a clear understanding of data flows and enabling them to manage and protect sensitive information effectively.

In terms of regional outlook, North America holds a significant share of the data mapping software market due to the presence of numerous technology giants and a high level of digital adoption across industries. The Asia Pacific region is anticipated to witness substantial growth during the forecast period, driven by rapid economic development, increasing digitalization, and the growing adoption of advanced data management solutions in countries like China and India.

The integration of a Sales Mapping System can significantly enhance the capabilities of data mapping software by providing a more comprehensive view of sales data across various channels. This system allows businesses to map sales data from different sources, such as e-commerce platforms, point-of-sale systems, and customer relationship management (CRM) tools, into a unified format. By doing so, organizations can gain deeper insights into their sales performance, identify trends, and make data-driven decisions to optimize their sales strategies. The Sales Mapping System also facilitates better alignment between sales and marketing teams, ensuring that both departments have access to accurate and up-to-date sales data. As a result, businesses can improve their overall sales effectiveness and drive revenue growth.

Component Analysis

When analyzing the data mapping software market by component, it is evident that the software segment dominates. This segment includes various data mapping tools and platforms designed to facilitate efficient data integration and management. The growing complexity of data environments and the need for advanced capabilities in data handling are driving the demand for sophisticated data mapping software. Companies are leveraging these tools to ensure seamless data flow across different systems, thereby enhancing operational efficiency and decision-making processes.

In addition to the software itself, services play a crucial role in the data mapping software market. These services include implementation, consulting, training, and support, which are essential for the successful deployment and utilization of data mapping solutions. Organizations often require expert guidance to customize and integrate data mapping software into their existing systems, ensuring optimal performance and compliance with regulatory requirements. The services segment is expected to grow in tandem with the software segment, as businesses increasingly seek comprehensive solutions th

Abstract copyright UK Data Service and data collection copyright owner. This project dealt with the phonetic details of intonation in Dutch and English. It focused on the alignment of intonational targets (e.g. local peaks and valleys) with the vowels and consonants in speech. Limited past research had suggested that this is systematic, but the factors that affect it are not well understood. The depositor's earlier research suggested that in many cases intonation targets are anchored to specific sounds (e.g. the beginning of the vowel following a stressed syllable). This kind of precision was rather unexpected, because investigators have concentrated on more variable effects (e.g. the closer a target is to the end of a word, the earlier it is aligned). The main goal of this project was to determine how general this anchoring is, what kind of landmarks (consonants, vowels, word ends, etc.) can serve as anchors, and how much the alignment of anchored targets can be affected by more variable factors. One practical motivation for this research was to provide the basic knowledge for improvements to synthetic speech. Most of the empirical research of the proposed project consisted of experiments in both English and Dutch, in which carefully selected sentences were read aloud and detailed acoustical measurements made of the speech. The depositor also studied short (5-10 minute) dialogues spoken under somewhat controlled conditions these are the Map Task dialogues deposited in this dataset. English and Dutch were chosen because their sound structures are similar enough that conclusions can be generalised from one language to the other, yet different enough that certain kinds of experimental controls can be used in one language which would be impossible in the other. Also, both languages support important speech technology industries. Main Topics: This corpus of natural Dutch conversation was collected as part of a project primarily concerned with the phonology and phonetics of intonation. The Map Task procedure for collecting spontaneous speech was used. The Map Task is a widely used tool in the study of dialogue, because it allows researchers to study conversations which are completely spontaneous and yet remarkably predictable and consistent. The task works as follows: the two participants to the conversation each have a map showing a variety of pictured landmarks with names like shepherd's hut or Green Mountain. The maps may differ slightly in detail; crucially, one map (the instruction giver's map) has a route marked on it; and the other (the instruction follower's map) does not. Neither speaker can see the other's map, and in some versions of the task (but not this one) the speakers cannot see each other. The task is for the instruction giver to explain to the instruction follower where the route passes, referring to the various landmarks along the way, accurately enough that the instruction follower can reproduce the route on his or her own map. The basic reference on the Map Task is Anderson et al, (1991), The HCRC Map Task Corpus, Language and Speech 34, 351-366. Further information on the Map Task is available at: http://www.hcrc.ed.ac.uk/dialogue/maptask.html The point of using the Map Task was to obtain natural productions of certain intonation patterns (e.g. various kinds of question intonation) which are difficult to obtain in reading experiments without explicitly instructing the speakers how to speak (and sometimes not even then). The most important manipulation of the maps was to select landmark names that manifested the phonological structures that the depositor was interested in, and that contained consonant types which would permit easy analysis of pitch patterns. However, the basic conversational task was unaffected by these manipulations, and conversations in the corpus are entirely comparable to those recorded in various languages elsewhere. So far as the depositor is aware, no other Map Task corpus exists in Dutch. The conversations were recorded at the phonetics laboratory of the University of Nijmegan on 5 February 1999 (day 1) and 8 February 1999 (day 2). In both cases a complete quad (4 speakers, 8 conversations) was recorded. The speakers were all students at the university. The maps were based on maps from the original HCRC Map Task. The distribution of the landmarks and the route on the giver's map were identical to the originals, but the actual names of the landmarks were in Dutch and in most cases the pictures had to be adapted as well.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The Vegetation Map of Cañada de San Vicente (CSV), San Diego County, was created by the California Department of Fish and Game (DFG) Vegetation and Mapping Program (VegCAMP). CSV, formerly known as Monte Vista Ranch, was acquired in April 2009 by DFG and is currently not open to the public as the management plan is not complete. The map study area boundary is based on the DFG Lands layer that was published in April, 2011 and includes 4888 acres of land. This includes 115 acres of private land located in the northeast corner of the map that was considered an area of interest (AOI) before purchase by DFG. The map is based on field data from 38 vegetation Rapid Assessment surveys (RAs), 111 reconnaissance points, and 118 verification points that were conducted between April 2009 and January 2012. The rapid assessment surveys were collected as part of a comprehensive effort to create the Vegetation Classification Manual for Western San Diego County (Sproul et al., 2011). A total of 1265 RAs and 18 relevés were conducted for this larger project, all of which were analyzed together using cluster analysis to develop the final vegetation classification. The CSV area was delineated by vegetation type and each polygon contains attributes for hardwood tree, shrub and herb cover, roadedness, development, clearing, and heterogeneity. Of 545 woodland and shrubland polygons that were delineated, 516 were mapped to the association level and 29 to the alliance level (due to uncertainty in the association). Of 46 herbaceous polygons that were delineated, 36 were mapped to the group or macrogroup level and 8 were mapped to association. Four polygons were mapped as urban or agriculture. The classification and map follow the National Vegetation Classification Standard (NVCS) and Federal Geographic Data Committee (FGDC) standard and State of California Vegetation and Mapping Standards. The minimum mapping area unit (MMU) is one acre, though occasionally, vegetation is mapped below MMU for special types including wetland, riparian, and native herbaceous and when it was possible to delineate smaller stands with a high degree of certainty (e.g., with available field data). In total, about 45 percent of the polygons were supported by field data points and 55 percent were based on photointerpretation.

Detailed geological mapping at 1:50 000 providing information on such themes as structural geology, mines and mineral deposits, topography and cultural features, hydrogeological features, graphic lithological logs, potential and perspective mines and open cuts and geomorphological units. The map was published in 1986. This data is held in GDA decimal degrees. Show full description

This dataset contains a habitat asset register for Ireland, e.g. a national scale habitat map conflating all nationally relevant habitat data into one dataset.

This dataset is part of a dataset series that establishes an ecosystem service maps (national scale) for a set of services prioritised through stakeholder consultation and any intermediate layers created by Environment Systems Ltd in the cause of the project. The individual dataset resources in the datasets series are to be considered in conjunction with the project report: https://www.npws.ie/research-projects/ecosystems-services-mapping-and-assessment

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland.

This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs.

The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.

This is a dataset download, not a document. The Open button will start the download.In 2015, the Oregon Biodiversity Information Center at Portland State University worked with the Oregon Department of Fish and Wildlife (ODFW), to assist in their 2015 conservation strategy update. This work involved updating the maps of each of ODFW’s conservation strategy habitats originally created for the first strategy in 2006,and integrating these into a 2015 strategy habitat map. The updated maps took advantage of new data and spatial modeling tools. However, strategy habitats only represent only 11 of the approximately 77 Oregon habitats, and are only mapped in the ecoregions in which they are conservation priorities. As a result, there was a strong interest in using this 2015 data to create a statewide, comprehensive habitat map. In 2017, the Oregon Department of Administrative Services, Geographic Enterprise Office (DAS-GEO), through their Framework Implementation program, with additional support from ODFW, funded the completion of a statewide habitat map, which was completed at the end of 2018. The habitat map is a compilation of a number of recent regional and ecosystem focused vegetation-mapping efforts. It includes the best available data for each of the habitat types. As a result, different parts of the map rely on varied methods and data. For detailed methodology please see the enclosed PDF document.

(Note: Updated inundation maps for 1:2 to 1:1000 floods are available from Alberta Environment and Parks (2020). The new draft maps can be viewed here: https://floods.alberta.ca/?app_code=FI&mapType=Draft) These inundation maps show whether a property is at risk for various sized river floods. The size of flood shown on this map has a 1/5 or a 20% chance of occurring in any year. The three distinct types of inundation shown on the maps are: o Inundation - Area flooded overland due to riverbank overtopping. o Isolated - Low lying areas that will not be wet from riverbank overtopping, but may experience groundwater seepage or stormwater backup. o Protected - Area protected by a permanent flood barrier. The flood areas shown are based on Alberta Environment and Parks most recent (2020) inundation maps. There is uncertainty inherent in predicting the effects of flood events, and this uncertainty increases for floods with less than a 1% chance of occurrence in any year. Any use of this data must recognizing the uncertainty with regards to the exact location and extent of flooding. More information on flood mapping for Calgary is available at https://calgary.ca/flood For Calgary's River Flood story, see: https://maps.calgary.ca/RiverFlooding/

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

Database GeoCR25 is a unique geographical information system developed by the on-going digitization of the geological maps at a scale of 1 : 25,000. It also contains database of the reference points and database of the unified geological legend.