The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

Maps are often animated to help users make comparisons and comprehend trends. However, large and complex differences between sequential maps can inhibit users from doing so. This paper proposes a morphing technique to highlight trends without manual intervention. Changes between sequential maps are considered as the diffusion processes of expanding classes, with these processes simulated by cellular automata. A skeleton extraction technique is introduced to handle special cases. Experimental results demonstrate that the proposed morphing technique can reveal obvious trends between dramatically changed maps. The potential application of the proposed morphing technique in sequential spatial data (e.g. remote-sensing images) is discussed.

This map draws attention to your thematic content by providing a neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground. This light gray map supports any strong colors, creating a visually compelling map graphic which helps your reader see the patterns intended. This map was developed by Esri using HERE data, DeLorme basemap layers, OpenStreetMap contributors, Esri basemap data, and select data from the GIS user community. Worldwide coverage is provided from Level 0 (1:591M scale) through Level 13 (1:72k scale). In North America (Canada, Mexico, United States), Europe, India, South America and Central America, Africa, most of the Middle east, and Australia & New Zealand coverage is provided from Level 14 (1:36k scale) through Level 16 (1:9k scale). For more information on this map, including the terms of use, visit us online.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independently of ArcMap, but output is then limited to a tabular report. The soil survey attribute database associated with the spatial soil map is a complicated database with more than 50 tables. Soil Data Viewer provides users access to soil interpretations and soil properties while shielding them from the complexity of the soil database. Each soil map unit, typically a set of polygons, may contain multiple soil components that have different use and management. Soil Data Viewer makes it easy to compute a single value for a map unit and display results, relieving the user from the burden of querying the database, processing the data and linking to the spatial map. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Resources in this dataset:Resource Title: Soil Data Viewer. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053620 Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independent of ArcMap, but output is then limited to a tabular report. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Links to download and install Download Soil Data Viewer 6.2 for use with ArcGIS 10.x and Windows XP, Windows 7, Windows 8.x, or Windows 10. Earlier versions are also available.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 245 atolls of the Pacific Ocean as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per country or region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

Map Drawing Services Market Outlook

The global map drawing services market size was valued at approximately $1.2 billion in 2023 and is projected to reach $2.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% during the forecast period. This growth can be attributed to the increasing demand for precise and customized mapping solutions across various industries such as urban planning, environmental management, and tourism.

One of the primary growth factors of the map drawing services market is the rapid advancement in Geographic Information Systems (GIS) technology. The integration of advanced GIS tools allows for the creation of highly accurate and detailed maps, which are essential for urban planning and environmental management. Additionally, the growing emphasis on smart city initiatives worldwide has led to an increased need for customized mapping solutions to manage urban development and infrastructure efficiently. These technological advancements are not only improving the quality of map drawing services but are also making them more accessible to a broader range of end-users.

Another significant growth factor is the rising awareness and adoption of map drawing services in the tourism sector. Customized maps are increasingly being used to enhance the tourist experience by providing detailed information about destinations, routes, and points of interest. This trend is particularly prominent in regions with rich cultural and historical heritage, where detailed thematic maps can offer tourists a more immersive and informative experience. Furthermore, the digitalization of the tourism industry has made it easier to integrate these maps into various applications, further driving the demand for map drawing services.

Environmental management is another key area driving the growth of the map drawing services market. With the increasing focus on sustainable development and environmental conservation, there is a growing need for accurate maps to monitor natural resources, track changes in land use, and plan conservation efforts. Map drawing services provide essential tools for environmental scientists and policymakers to analyze and visualize data, aiding in better decision-making and management of natural resources. The rising environmental concerns globally are expected to continue driving the demand for these services.

From a regional perspective, North America is anticipated to hold a significant share of the map drawing services market due to the high adoption rate of advanced mapping technologies and the presence of major market players in the region. Furthermore, the region's focus on smart city projects and environmental conservation initiatives is expected to fuel the demand for map drawing services. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate, driven by rapid urbanization, industrialization, and the growing need for efficient infrastructure planning and management.

Service Type Analysis

The map drawing services market is segmented into several service types, including custom map drawing, thematic map drawing, topographic map drawing, and others. Custom map drawing services cater to specific client needs, offering tailored mapping solutions for various applications. This segment is expected to witness significant growth due to the increasing demand for personalized maps in sectors such as urban planning, tourism, and corporate services. Businesses and government agencies are increasingly relying on custom maps to support their operations, leading to the expansion of this segment.

Thematic map drawing services focus on creating maps that highlight specific themes or topics, such as population density, climate patterns, or economic activities. These maps are particularly useful for educational purposes, research, and community planning. The growing emphasis on data-driven decision-making and the need for visual representation of complex datasets are driving the demand for thematic maps. Additionally, thematic maps play a crucial role in public health, disaster management, and policy formulation, contributing to the segment's growth.

Topographic map drawing services offer detailed representations of physical features of a landscape, including elevation, terrain, and landforms. These maps are essential for various applications, such as environmental management, military ope

ABSTRACT Owing to the exponential growth of the human population and problems related to food supply, research focused on finding the most suitable approach to manage and geographically explore the environment using sustainable technologies stand out. The present study aims to produce a consistent interpolation of historical series of enthalpy (H) resulting in a thematic map of enthalpy, using the spline method as a kriging option in areas with few sampling points. The thematic map considers thermal comfort conditions to produce broiler chickens, that could be used as a management tool to reduce power consumption due to the cooling process of the facilities. It was verified that spline is an efficient method to create a suitable thematic maps representations of areas presenting a few sampled units. The geographical representation of enthalpy allowed the evaluation of the environments, concluding that the state of Mato Grosso do Sul, Brazil is inadequate for broiler chickens production without suitable thermal cooling systems. Evidence suggests introduction of aviculture in areas still unexplored, e.g., Chapadão do Sul and Sete Quedas.

The feature class MO_scostamenti_poly_8 represents the deviations, polygonal elements acquired from the map of deviations and incompatibilities in scale 1:25 000. The maps PTPAAV (Territorial Environmental Country Plan of Area Vasta) are a series of thematic maps drawn up since 1989 and finished and approved at the end of November 1991, are divided into territorial areas for a total of 8 areas identified on the regional territory. The work was carried out by several groups of technicians, a coordination group which established by circulars the standards to be used for the drafting of plans ranging from the thickness of the graph tip to the type of retino and the nuances to be used, and 8 design groups one for each area, which have created the maps trying to standardise spatial information as much as possible. The paperwork of this work was delivered to us in 2008 by the Environmental Heritage Office of the Molise Region. The latter already had scans of some thematic cards related to some areas, the missing ones and in the case of scans not found suitable for georeference, have been scanned. The mapping basis used by the working groups for the creation of PTPAAV maps was the IGM on a scale of 1:25,000.

This service allows users to highlight and superimpose the forest fire thematic maps created by the DPNM-MATTM through various agreements with research bodies as well as the polygons of the fires detected with GPS by the State Forestry Corps in the National Parks.

GISCO (Geographic Information System of the COmmission) is responsible for meeting the European Commission's geographical information needs at three levels: the European Union, its member countries, and its regions.

In addition to creating statistical and other thematic maps, GISCO manages a database of geographical information, and provides related services to the Commission. Its database contains core geographical data covering the whole of Europe, such as administrative boundaries, and thematic geospatial information, such as population grid data. Some data are available for download by the general public and may be used for non-commercial purposes. For further details and information about any forthcoming new or updated datasets, see http://ec.europa.eu/eurostat/web/gisco/geodata.

This metadata refers to the whole content of GISCO reference database extracted in June 2020, which contains both public datasets (also available for the general public through http://ec.europa.eu/eurostat/web/gisco/geodata) and datasets to be used only internally by the EEA (typically, but not only, GISCO datasets at 1:100k). The document GISCO-ConditionsOfUse.pdf provided with the dataset gives information on the copyrighted data sources, the mandatory acknowledgement clauses and re-dissemination rights. The license conditions for EuroGeographic datasets in GISCO are provided in a standalone document "LicenseConditions_EuroGeographics.pdf".

The database is provided in GPKG files, with datasets at scales from 1:60M to 1:100K, with reference years spanning until 2021 (e.g. NUTS 2021). Attribute files are provided in CSV. The database manual, a file with the content of the database, a glossary, and a document with the naming conventions are also provided with the database.

The main updates with respect to the previous version of the full database in the SDI (from Jul. 2018) are the addition of the following datasets: - Administrative boundaries at country level, 2020 (CNTR_2020) - Administrative boundaries at commune level, 2016 (COMM_2016) - Coastline boundaries, 2016 (COAS_2016) - Exclusive Economic Zones, 2016 (EEZ_2016)

- Farm Accountancy Data Network based on NUTS 2016, 2018 (FADN_2018)

             Local Administrative Units, 2018 (LAU_2018)

• Nomenclature of Territorial Units for Statistics, 2021 (NUTS_2021)
• Political regions (NB.: defined by the Committee of the Regions), 2018 (POLREG_2018)
• Pan-European Settlements, 2016 (STLL_2016) and 2018 (STLL_2018)
• Transport Networks (NB.: railway lines, railway stations, roads, road junctions, levelcrossings, ferry routs and custom points), 2019 (TRAN_2019)
• Urban Audit Areas, 2018 (URAU_2018) and 2020 (URAU_2020)

NOTE: This metadata file is only for internal EEA purposes and in no case replaces the official metadata provided by Eurostat. For specific GISCO datasets included in this version there are individual EEA metadata files in the SDI: NUTS_2021 and CNTR_2020. For other GISCO datasets in the SDI, it is recommended to use the version included in this dataset. The original metadata files from Eurostat for the different GISCO datasets are available via ECAS login through the Eurostat metadata portal on https://webgate.ec.europa.eu/inspire-sdi/srv/eng/catalog.search#/home. For the public products metadata can also be downloaded from https://ec.europa.eu/eurostat/web/gisco/geodata. For more information about the full database or any of its datasets, please contact the SDI Team (sdi@eea.europa.eu).

Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. 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. Note: All Minnesota SSURGO data sets are now in Version 2 format. This format is compatible with NRCS's Soil Data Viewer, a free extension for ArcView 3.x that allows users to more easily create soil-based thematic maps. - For more information about Soil Data Viewer, see http://www.itc.nrcs.usda.gov/soildataviewer/ Also note: This metadata record was created by the Minnesota Land Management Information Center to serve as a generic record for all SSURGO data sets within Minnesota. See the individual county metadata records created by NRCS for county-specific information; these records are included in the data set download files.

Australian Mineral Blocks (2014) - Aligned with the current Australian Maritime Boundary Dataset (AMB2014).

ESRI Geodatabase.

The dataset was created by Geoscience Australia using the framework described in Section 17 of the Offshore Minerals Act 1994.

The international, scheduled areas and coastal waters used in this dataset are those found in the current Australian Maritime Boundary Dataset 2014 (AMB2014).

The dataset is comprised of both polygons and points created to very high precision, accurate to within millimetres.

The blocks have been cut by Australia's international boundaries, the scheduled areas and the coastal waters. Each block is assigned a polygon, including partial blocks. All blocks are titled with their block ID, and a list of vertices that make up the blocks. Each vertex of the dataset is also replicated as a discrete point in the points dataset.

The design of the dataset allows for the exact location of every vertex to be known to millimetre precision. The corner coordinates of blocks are now defined to a high precision, and can be found by querying the appropriate point.

The blocks are attributed with fields containing information on: Block ID Parent 1 Million Mapsheet Offshore Area Epoch of the boundaries used to cut the data AMB2014 Datum Origin of the mapsheet in AGD66 The position of all vertices in the block The number of vertices in the block The area of the block in acres The area of the block in hectares The calculation used to find the area of the blocks is estimated to be precise to better than 1%. This is considered to be sufficient as under the permit and licensing arrangements in the Offshore Minerals Act, the area of a block has no relevance. Therefore the area figure is provided solely for reference.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

From gridded National Soil Survey Geographic Database (gNATSGO). Used Soil Data Development Toolbox > gSSURGO Mapping Toolset > Create Soil Map Tool, Exported Data Layer to TIFF, and Used Spatial Analyst > Reclass > Lookup Tool to create this data layer and display the HYDROLGRP_. Follow instructions in "How to Create an On-Demand Soil Property or Interpretation Grid from gNATSGO". Shows sSSURGO data for California. A - sand, loamy sand, sandy loam B - loam, silt, loam or silt C - sandy clay loam D - clay loam, silty clay loam, sandy clay, silty clay, or clay The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The state-wide gNATSGO databases contain a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. Please note that for the CONUS database, only a 30 meter raster is included. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. Click here for the current completion status of SSURGO mapping. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625) Use the Create A Soil Map ArcTool from the gSSURGO Mapping Toolset in the Soil Data Development Toolbox to make a TIFF data layer (Instructions: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625#grid). Make a Hydrological Soils Group Map, and display it using the Hydrolgrp_ attribute. NotesThe SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year. gNATSGO is an ESRI file geodatabase. The soil map units are delivered only as a 10-meter raster version and are uniquely identified by the mukey, which is included in the attribute table. No vectorized version of the soil map units is included in gNATSGO. The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables. The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field. The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS. A gNATSGO database was created for the conterminous United States and for each state or island territory that does not have complete coverage in SSURGO or has a published RSS. If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

Australian Mineral Blocks (2020) - Aligned with the current Australian Maritime Boundary Dataset (AMB2020).

ESRI Geodatabase.

Available for download as GDA94 or GDA2020.

The dataset was created by Geoscience Australia using the framework described in Section 17 of the Offshore Minerals Act 1994.

The international, scheduled areas and coastal waters used in this dataset are those found in the current Australian Maritime Boundary Dataset 2020 (AMB2020). The 2020 release has been updated to reflect the 2018 Timor Sea Treaty.

The dataset is comprised of both polygons and points created to very high precision, accurate to within millimetres.

The blocks have been cut by Australia's international boundaries, the scheduled areas and the coastal waters. Each block is assigned a polygon, including partial blocks. All blocks are titled with their block ID, and a list of vertices that make up the blocks. Each vertex of the dataset is also replicated as a discrete point in the points dataset.

The design of the dataset allows for the exact location of every vertex to be known to millimetre precision. The corner coordinates of blocks are now defined to a high precision, and can be found by querying the appropriate point.

The blocks are attributed with fields containing information on: Block ID Parent 1 Million Mapsheet Offshore Area Epoch of the boundaries used to cut the data AMB2014 Datum Origin of the mapsheet in AGD66 The position of all vertices in the block The number of vertices in the block The area of the block in acres The area of the block in hectares The calculation used to find the area of the blocks is estimated to be precise to better than 1%. This is considered to be sufficient as under the permit and licensing arrangements in the Offshore Minerals Act, the area of a block has no relevance. Therefore the area figure is provided solely for reference.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

The feature class MO_vincolo_usi_civici_poly_2 represents the areas subject to civic use constraint, elements acquired from the constraint map on a scale of 1:25 000. The maps PTPAAV (Territorial Environmental Country Plan of Area Vasta) are a series of thematic maps drawn up since 1989 and finished and approved at the end of November 1991, are divided into territorial areas for a total of 8 areas identified on the regional territory. The work was carried out by several groups of technicians, a coordination group which established by circulars the standards to be used for the drafting of plans ranging from the thickness of the graph tip to the type of retino and the nuances to be used, and 8 design groups one for each area, which have created the maps trying to standardise spatial information as much as possible. The paperwork of this work was delivered to us in 2008 by the Environmental Heritage Office of the Molise Region. The latter already had scans of some thematic cards related to some areas, the missing ones and in the case of scans not found suitable for georeference, have been scanned. The mapping basis used by the working groups for the creation of PTPAAV maps was the IGM on a scale of 1:25,000.

This map is suitable only for the ArcGIS Online Map Viewer. Because of viewer-specific effects, it will not render as designed in Map Viewer Classic or ArcGIS Pro.This map uses a combination of blend modes and effects with imagery, hillshade, and reference layers, to create a muted landcover-tinted natural appearance. It is appropriate for reference mapping, physical/environmental geography themes, or thematic maps where a muted representation of the natural environment provides helpful context.