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

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The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled: "Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review) Abstract: To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration. This database was compiled from the following sources: 1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency. source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip 2. NMD. National Land Cover Data. Swedish Environmental Protection Agency. source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/ 3. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance. source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/ 4. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland. source: https://glad.earthengine.app 5. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN). source: https://doi.org/10.6084/m9.figshare.9828827.v2 6. POPULATION. Total Population in Sweden. Statistics Sweden. source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/ To learn more about the GRASS GIS database structure, see: https://grass.osgeo.org/grass82/manuals/grass_database.html

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

U.S. soil property maps in a raster format that meet the GlobalSoilMap standards. Services: GlobalSoilMap_v05/available_water_supply (MapServer) GlobalSoilMap_v05/bulk_density_lessthan_2mm (MapServer) GlobalSoilMap_v05/bulk_density_whole_soil (MapServer) GlobalSoilMap_v05/clay (MapServer) GlobalSoilMap_v05/effective_cation_exchange_capacity (MapServer) GlobalSoilMap_v05/electric_conductivity (MapServer) GlobalSoilMap_v05/gravel (MapServer) GlobalSoilMap_v05/pH (MapServer) GlobalSoilMap_v05/sand (MapServer) GlobalSoilMap_v05/silt (MapServer) GlobalSoilMap_v05/soil_depth (MapServer) GlobalSoilMap_v05/soil_organic_carbon (MapServer) Resources in this dataset:Resource Title: GlobalSoilMaps. File Name: Web Page, url: https://nrcsgeoservices.sc.egov.usda.gov/arcgis/rest/services/GlobalSoilMap_v05 ArcGIS REST Services Directory Folder: GlobalSoilMap_v05

The market for Geographic Information Systems (GIS) solutions is projected to reach a staggering XXX million by 2033, growing at a remarkable CAGR of XX% from 2025 to 2033. This growth is driven by the increasing adoption of GIS technology across various industries, including transportation, AEC, telecommunications, agriculture, and entertainment. GIS solutions provide valuable insights by overlaying data onto geographic maps, helping businesses make informed decisions, optimize operations, and enhance customer experiences. Moreover, the growing awareness of sustainability and the need for environmental conservation is further fueling the demand for GIS solutions in sectors such as utilities, environmental consulting, and urban planning. The GIS market is segmented based on type (software, service), application, and region. North America dominates the market, followed by Europe and Asia Pacific. Key players in the GIS industry include Esri, Pro GIS Solutions, GBS, Fugro, DataVoice, Pontech, ABPmer, VertiGIS, Tata Communications, GIS Solutions, Inc, CGIS Solutions, and Spectus. The market is characterized by intense competition, ongoing advancements in technology, and the emergence of specialized GIS solutions. As businesses recognize the transformative potential of GIS technology, the market is expected to continue to experience robust growth in the coming years.

GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

The Gap Analysis Program (GAP) is an element of the U.S. Geological Survey (USGS). GAP helps to implement the Department of Interior?s goals of inventory, monitoring, research, and information transfer. GAP has three primary goals: 1 Identify conservation gaps that help keep common species common; 2 Provide conservation information to the public so that informed resource management decisions can be made; and 3 Facilitate the application of GAP data and analysis to specific resource management activities. To implement these goals, GAP carries out the following objectives: --Map the land cover of the United States --Map predicted distributions of vertebrate species for the U.S. --Map the location, ownership and stewardship of protected areas --Document the representation of vertebrate species and land cover types in areas managed for the long-term maintenance of biodiversity --Provide this information to the public and those entities charged with land use research, policy, planning, and management --Build institutional cooperation in the application of this information to state and regional management activities. GAP provides the following data and web services: The Protected Areas Database of the United States (PAD-US) is a geodatabase that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The PADUS GAP Status Layer web service can be found at http://gis1.usgs.gov/arcgis/rest/services/gap/PADUS_Status/MapServer . The Land Cover Data creates a seamless data set for the contiguous United States from the four regional Gap Analysis Projects and the LANDFIRE project. The Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx . In addition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer The GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). The GAP species distribution models represent the areas where species are predicted to occur based on habitat associations. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/

GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were based on land cover data of ecological systems and--when applicable for the given taxon--on ancillary variables such as elevation, hydrologic characteristics, human avoidance characteristics, forest edge, ecotone widths, etc. Distribution models were generated using a python script that selects model variables based on literature cited information stored in a wildlife habitat relationship database (WHRdb); literature used includes primary and gray publications. Distribution models are 30 meter raster data and delimited by GAP species ranges. Distribution model data were attributed with information regarding seasonal use based on GAP regional projects (NWGAP, SWReGAP, SEGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/ GAP used the best information available to create these species distribution models; however GAP seeks to improve and update these data as new information becomes available. Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Distribution Models. Available: http://gapanalysis.usgs.gov. Accessed [date].

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

1. Cultural Ecosystem Services (CES) encompass a range of social, cultural and health benefits to local communities, for example recreation, spirituality, a sense of place and local identity. However, these complex and place-specific CES are often overlooked in rapid land management decisions and assessed using broad, top–down approaches. 2. We use the Toolkit for Ecosystem Service Site-based Assessment (TESSA) to examine a novel approach to rapid assessment of local CES provision using inductive, participatory methods. We combined free-listing and participatory geographic information systems (GIS) techniques to quantify and map perceptions of current CES provision of an urban green space. The results were then statistically compared with those of a proposed alternative scenario with the aim to inform future decision-making. 3. By identifying changes in the spatial hotspots of CES in our study area, we revealed a spatially-specific shift toward positive sentiment regarding several CES under the alternative state with variance across demographic and stakeholder groups. Response aggregations in areas of proposed development reveal previously unknown stakeholder preferences to local decision-makers and highlight potential trade-offs for conservation management. Free-listed responses revealed deeper insight into personal opinion and context. 4. This work serves as a useful case study on how the perceptions and opinions of local people regarding local CES could be accounted for in the future planning of an urban greenspace and how thorough analysis of CES provision is important to fully-inform local-scale conservation and planning for the mutual benefit of local communities and nature.

Open the Data Resource: https://gis.chesapeakebay.net/cross-git/overview/ This story map summarizes the data assembled and the scoring criteria recommended by the subject matter experts involved in the Chesapeake Bay Program's Cross-GIT Mapping Project. It also presents the composite results of the analyses. Access the Cross-GIT HUC-12 Conservation Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Cons_Composite/MapServer Access the Cross-GIT HUC-12 Restoration Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Rest_Composite/MapServer

Aquatic Mapping Service Market Outlook

The Aquatic Mapping Service Market is projected to achieve a robust market size by 2032, growing from approximately USD 1.5 billion in 2023 at a compound annual growth rate (CAGR) of 7.2%. This growth can be primarily attributed to the increased demand for high-resolution underwater data to support various applications such as environmental monitoring, marine spatial planning, and aquaculture management. The growing need for sustainable water resource management and the increasing emphasis on environmental conservation are other key factors propelling the market forward. The advent of advanced technologies such as GIS, sonar, and remote sensing further accelerates the demand for precise aquatic mapping solutions.

One of the primary drivers of growth in the aquatic mapping service market is the rising global awareness and commitment toward environmental preservation. As ecosystems face threats from pollution, climate change, and overfishing, the need for comprehensive data on aquatic environments becomes paramount. Bathymetric and habitat mapping services provide essential insights into underwater topographies and biological habitats, aiding in the formulation of effective conservation strategies. Additionally, governments and environmental agencies are increasingly investing in marine spatial planning to sustainably manage ocean and coastal resources, which in turn boosts the demand for aquatic mapping services.

Another significant growth factor is the burgeoning aquaculture industry, which requires precise mapping for the optimal placement of fish farms and the monitoring of water quality. With the world's population continuously growing, the demand for seafood as a sustainable protein source is on the rise, leading to the expansion of aquaculture activities globally. Consequently, the market for aquatic mapping services is poised to grow as aquaculture operators rely on these services to ensure operational efficiency and environmental compliance. The integration of advanced technologies such as sonar and GIS allows for more accurate and comprehensive data collection, further enhancing the market's appeal.

Technological advancements play a crucial role in driving market growth, with innovations in remote sensing and underwater imaging enhancing the capabilities of aquatic mapping. These technologies enable the collection of high-resolution data over large areas and difficult-to-reach underwater locations. As a result, industries such as oil and gas, marine engineering, and coastal development increasingly depend on these technological advancements to support their operations. The integration of AI and machine learning in data analysis also enhances the processing of complex datasets, providing insights that inform decision-making and strategic planning in various sectors.

Regionally, the market for aquatic mapping services exhibits diverse growth patterns. North America currently dominates the market due to its advanced technological infrastructure and significant investment in environmental conservation initiatives. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid industrialization, increasing government initiatives for sustainable resource management, and the expansion of the aquaculture industry. Europe also presents significant opportunities, particularly in marine spatial planning and environmental monitoring, owing to stringent regulatory frameworks aimed at protecting marine environments.

Service Type Analysis

In the aquatic mapping service market, service type segmentation plays a pivotal role in understanding the breadth of applications and the specific areas experiencing growth. Bathymetric mapping, which involves the measurement of underwater topography, remains a cornerstone of this market segment. This service is critical for various applications, including the construction of marine infrastructure, such as ports and offshore platforms, as well as environmental monitoring. The demand for bathymetric mapping is spurred by the need for detailed underwater terrain models, which are essential for safe navigation and disaster management. Advances in sonar and LiDAR technologies have significantly enhanced the precision and efficiency of bathymetric mapping, making it more accessible and cost-effective for clients across different sectors.

Habitat mapping is another vital service within the aquatic mapping domain, focusing on the identification and analysis of biological habitats underwater. This service is increasingly sought after f

Data Sources:Fisheries and Oceans CanadaCanada’s marine protected and conserved area:https://egisp.dfo-mpo.gc.ca/arcgis/rest/services/open_data_donnees_ouvertes/oceans_act_marine_protected_areas_zones_de_protection_marines_de_la_loi_sur_les_oceans/MapServerhttps://egisp.dfo-mpo.gc.ca/arcgis/rest/services/open_data_donnees_ouvertes/other_effective_area_based_conservation_measures/MapServer

Conservation of Arctic Flora and Fauna (CAFF):Bioclimatic subzones of the Arctic territory according to the CAVM https://geo.abds.is/geonetwork/srv/eng/catalog.search#/metadata/7856ef8b-458f-4c2b-a95b-9ebc7a4cb217Esri Canada Basemaphttps://esrica-ncr.maps.arcgis.com/home/item.html?id=506ddbf4b83445c98a5d0b7713c62fb1https://esrica-ncr.maps.arcgis.com/home/item.html?id=16029291813f487aa17afc7fb0800d82 thumbnail image: https://news.illinois.edu/view/6367/750016

Spatial data for the potential landscapes in Bulgaria

GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). To represent these geographic limits, GAP compiled existing GAP data, where available, and NatureServe data (Patterson et al. 2003, Ridgely et al. 2007, NatureServe 2010) IUCN data (IUCN 2004), where needed. Data provided by GAP in collaboration with the Northwest Gap Analysis Project (NWGAP), the Southwest Regional Gap Analysis Project (SWReGAP), the Southeast Gap Analysis Project (SEGAP), the Alaska Gap Analysis Project (AKGAP), the Hawaii Gap Analysis Project (HIGAP), the Puerto Rico Gap Analysis Project (PRGAP), and the U.S. Virgin Islands Gap Analysis Project (USVIGAP).

Web map services for species ranges can be accessed via:

http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds

http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals

http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians

http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles

A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/

Bird data provided by NatureServe in collaboration with Robert Ridgely, James Zook, The Nature Conservancy's Migratory Bird Program, Conservation International's Center for Applied Biodiversity Science (CABS), World Wildlife Fund US, and Environment Canada's WILDSPACE.

Mammal data provided by NatureServe in collaboration with Bruce Patterson, Wes Sechrest, Marcelo Tognelli, Gerardo Ceballos, The Nature Conservancy's Migratory Bird Program, Conservation International's CABS, World Wildlife Fund US, and Environment Canada's WILDSPACE.

Reptile data were provided by the International Union for Conservation of Nature and Natural Resources (IUCN).

Amphibian data developed as part of the Global Amphibian Assessment and provided by IUCN-World Conservation Union, Conservation International and NatureServe.

Once the needed range data were compiled it was intersected with Natural Resource Conservation Service National Watershed Boundary dataset of 12-digit hydrological units for the US (U.S. Geological Survey and U.S. Department of Agriculture, Natural Resources Conservation Service 2009). Range data were attributed with information regarding occurrence/presence, origin, reproductive use, and seasonal use from GAP regional projects (SWReGAP, SEGAP, NWGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data.

GAP used the best information available to create these species ranges; however GAP seeks to improve and update these data as new information becomes available. These species range data provide the biological context within which to build our species distribution models.

Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Ranges. Available: http://gapanalysis.usgs.gov. Accessed [date]

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

Aim: To identify potential landscapes for the conservation of the black-tailed prairie dog (BTPD) ecosystem, across their historical geographic range within the United States.

Location: Central Grasslands of the United States.

Methods: We used a structured decision analysis approach to identify landscapes with high conservation potential (HCP) for the BTPD ecosystem. Our analysis incorporated ecological, political, and social factors, along with changing climate and land use to maximize long-term conservation potential. We created scenarios that involved current and future projected suitable BTPD habitat, across the BTPD range within the United States. These were our RANGEWIDE scenarios. Additionally, because conservation policies and funding decisions are often made by political entities, we also identified STATE-LEVEL conservation priorities, under both present and projected future climate. Our STATE-LEVEL analysis sought conservation solutions within each of the states’ boundaries only, so do not consider a rangewide perspective.

Results: The landscapes we identified with HCP (top 30% range-wide) represented 22% of the historical distribution of black-tailed prairie dogs and remained strongholds under projected climate change. We provide a suite of HCP area scenarios to help inform different conservation and management interests, including those that consider projected climate change and jurisdictional (state-level) boundaries. STATE-LEVEL conservation priorities differed considerably from RANGEWIDE priorities, under both current and future climate scenarios. The largest difference was among the southern states (Arizona, New Mexico, and Texas), where climate change reduces the conservation priorities across this region more when viewed from a RANGEWIDE perspective than when viewed from a STATE-LEVEL perspective. Additionally, from a RANGEWIDE perspective, the eastern states have fewer areas with HCP compared to the western states within the BTPD range, but when viewed from a STATE-LEVEL perspective there are considerably more areas with HCP. We expected such differences because this question was aimed at understanding the HCP areas within each state, so the analysis was seeking conservation solutions within each of the states’ boundaries. Identifying STATE-LEVEL conservation priorities is important because funding sources and management priorities are often focused at the state-level, and not range-wide. This way, each state has information on conservation priorities within their own jurisdictional boundaries. We suggest each state focus conservation efforts for the BTPD ecosystem in those areas that remain priorities into the future at the STATE-LEVEL, while also considering those priorities identified within their state under the RANGEWIDE perspective.

Main Conclusions: Our findings highlight the large conservation potential for BTPDs and associated species, and the maps we generated can be incorporated into other large-scale, multi-species conservation planning efforts being developed for the Central Grasslands of North America.

Digital Mapping Cameras Market Outlook

The global digital mapping cameras market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to reach USD 4.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This robust growth is primarily driven by technological advancements in camera systems, increasing demand for precise and high-resolution aerial photogrammetry, and a rising need for geographic data across various industries. As industries continue to harness the power of digital mapping for applications ranging from urban planning to environmental monitoring, the demand for cutting-edge mapping cameras is expected to witness significant uptick.

One of the primary growth factors fueling the digital mapping cameras market is the continuous evolution of technology that allows for improved image resolution and accuracy. With the development of advanced sensors, lenses, and data processing tools, digital mapping cameras are now capable of capturing highly detailed images that can be used for a variety of applications such as 3D modeling, geographic information systems (GIS), and infrastructure development. Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms in digital mapping has enhanced the ability to analyze and interpret vast amounts of geographic data efficiently. This technological progression not only enhances the capabilities of mapping cameras but also expands their application scope, thus driving market growth.

The increasing emphasis on smart city initiatives and urban planning is also a significant driver of market expansion. Governments and urban planners are increasingly relying on digital mapping to plan and develop infrastructure that can support growing urban populations. Digital mapping cameras provide the detailed geographic information necessary for planning transportation networks, utility services, and sustainable city layouts. As cities strive to become smarter and more sustainable, the demand for high-quality digital mapping solutions is set to rise. This trend is particularly noticeable in developing regions where urbanization is occurring at a rapid pace, thereby contributing to the growth of the digital mapping cameras market.

Moreover, the growing need for environmental monitoring and management is bolstering the demand for digital mapping cameras. With increasing awareness of climate change and environmental degradation, governments and environmental organizations are utilizing digital mapping technologies to monitor and manage natural resources effectively. Applications such as deforestation tracking, wildlife habitat analysis, and disaster management benefit immensely from high-resolution digital mapping imagery. This demand for environmental applications is expected to further stimulate market growth as organizations seek to leverage digital mapping technologies for sustainable development and conservation efforts.

Digital Elevation Models (DEMs) play a crucial role in enhancing the capabilities of digital mapping cameras. These models provide a 3D representation of a terrain's surface, which is essential for accurate geographic analysis and planning. By integrating DEMs with digital mapping technologies, users can achieve a more comprehensive understanding of the landscape, enabling precise measurements and detailed topographical insights. This integration is particularly beneficial for applications such as urban planning, where understanding elevation changes can inform infrastructure development and flood risk assessments. As the demand for high-resolution geographic data continues to grow, the use of Digital Elevation Models is expected to become increasingly prevalent, driving further advancements in digital mapping solutions.

Product Type Analysis

The digital mapping cameras market is segmented by product type into frame cameras, line-scan cameras, and others. Frame cameras are a traditional choice in digital mapping, known for capturing images in a frame-by-frame manner, which is particularly useful for aerial surveying and photogrammetry. These cameras offer high resolution and are widely used in applications requiring detailed geographic data. Frame cameras are often equipped with advanced image stabilization and processing features that ensure clear and accurate images, making them indispensable in the cartographic process. Their high versatility and robust performance in capturing detailed images contribute to their signifi

◦Overview: A key principle of Landscape Conservation Design is that “Stakeholders design landscape configurations that promote resilient and sustainable social-ecological systems” (Campellone et al, 2018). From Campellone et al: (2018): “A beneficial aspect of stakeholder engagement in spatial design is the development of a deeper trust that the models used to identify priorities integrate their interests with other information and knowledge, which furthers social learning and collective agreement on resource allocation and landscape objectives” (Melillo et al., 2014). Overall, the co-development of a spatial design helps organize landscape elements while maintaining and improving stakeholder buy-in” (De Groot, Alkemade, Braat, Hein, & Willemen, 2009; Melillo et al., 2014).”◦Analytical Question: Create a prototype landscape design (blueprint) that integrates multiple values on the landscape including wildlife conservation, forest and agriculture production, recreation, cultural and human health. The prototype will be created based upon readily available data.This analysis will be used to understand landscape-scale conservation and working landscape priorities, while incorporating other important values.The blueprint will be used to represent a sustainable landscape in the future.◦Desired Outcome: A map or maps that represents a balance of multiple values on the landscape, with a focus on conservation and working landscape values.