To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

This dataset contains files produced by fMRIPrep that allow to transform the fMRI data between different spaces. For instance, any results obtained in the subjects' individual anatomical space could be transformed into the MNI standard space, allowing to compare results between subjects or even with other datasets.Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior.See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

GIS applications that link natural language text to geographic space using gazetteers are essential for managing spatial data in archived geological records. However, these applications face limitations due to limited gazetteer scope of coverage. Crowdsourced gazetteers often have global coverage, making them excellent reference data for resolving spatial details in records, including converting textual descriptions of locations to GPS features. This can be useful for rectifying missing GPS information in field-captured geological records, especially those obtained from remote and hard-to-reach areas. However, accurately transforming location descriptions in text to GPS coordinates is challenging, and reference data quality can be crucial in minimizing errors and uncertainties. A list of mineral specimen localities referencing geological sampling sites in the Northwest Territories and Nunavut were geoparsed using Geonames and OpenStreetMap geocoders and match rates, positional accuracy, and lexical similarity were quantified to assess performance.

This dataset was derived from the primary ‘SLATS Landsat woody change data (25m) for 1988 – 2010’ raster (grid) layers used to generate the annualised woody vegetation change rates for the 2010 NSW Annual Report of Native Vegetation. This data describes the areas and type of woody vegetation change (loss) based on the analysis of multi-date Landsat imagery covering NSW.

This data is based on a biennial LANDSAT coverage between 1988-2006 and annual coverage 2006-2010. LANDSAT Imagery 1988-2008 was processed by Geoscience Australia at 25m resolution.

2008 onwards is based on USGS processed LANDSAT at 30m resolution. Note, this vector data may generate slightly different aerial statistics to those generated from the source raster data. This is due to variation caused by the data transformation and vector cleaning processes applied in generating the vector data.

VDatum is a free software tool being developed jointly by NOAA's National Geodetic Survey (NGS), Office of Coast Survey (OCS), and Center for Operational Oceanographic Products and Services (CO-OPS). VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums -

The NSDI Geoportal serves as a starting point for accessing spatial data sources that are, according to the NSDI Act (Official Gazette 56/2013), part of National Spatial Data Infrastructure.
Pursuant to Article 11 of this Act, the NSDI Geoportal is established, maintained and developed by the National Contact Point for the purpose of metadata management and provision of the services of spatial data viewing, download, transformation and retrieval, as well as other information related to National Spatial Data Infrastructure.
The portal includes data and metadata related to Climate Change, Food Security, Raw Materials and Energy thematic areas.

The iotrans extension enhances CKAN's capabilities by allowing users to convert datastore resources into various file formats and, for spatial data, transform them between Coordinate Reference Systems (EPSG). This extension addresses the need to download datastore resources in multiple formats and projections beyond CKAN's built-in options, leveraging Python libraries for data conversion. It provides CKAN actions to facilitate file format conversion and data transformation, primarily intended for administrative users. Key Features: Datastore Resource Conversion: Converts CKAN datastore resources to various formats, including CSV, GeoJSON, GPKG, SHP, JSON, and XML. Coordinate Reference System Transformation: Transforms spatial data between different EPSG codes, enabling data compatibility across various GIS applications. Admin-Only Actions: Introduces two CKAN actions, to_file and prune, accessible only to administrator users for file conversion and temporary file cleanup. Disk-Based Processing: Streams data from the CKAN datastore to a temporary CSV file, reducing memory consumption during format conversion. Spatial Data Handling: Identifies spatial data based on the presence of a "geometry" attribute and converts non-Multi geometry types to their Multi counterparts (e.g., Point to MultiPoint) to ensure consistent geometry types within output files. Shapefile Support: Handles shapefile-specific limitations by truncating column names longer than 10 characters, creating unique column names, and documenting the original-to-truncated name mapping in a zipped text file within the shapefile. Temporary File Management: Uses the /tmp directory as a staging area for file conversions, with a prune action to remove files or directories within this location. Technical Integration: The iotrans extension functions by adding new API actions to CKAN (to_file and prune). These actions interact directly with the CKAN datastore extension, retrieving data in chunks via sequential calls to CKAN's datastore_search API, converting it to different formats, and storing these in the /tmp directory. It requires the CKAN Datastore extension to be active. Benefits & Impact: The iotrans extension streamlines the process of extracting and transforming data from the CKAN datastore, enabling users to easily access data in preferred formats and coordinate reference systems. This enhances data usability and interoperability, making it easier to integrate CKAN data into other applications and workflows. It is especially useful for organizations that need to provide data in various formats to meet diverse user needs.

This dataset is a land use classification method that uses multidimensional feature index coupled decision tree correction. The classification results were applied in the silt coastal zone of the Hangzhou Bay coastal plain, covering land use classification information at five key time points (2000, 2007, 2015, 2020, 2024) from 2000 to 2024. The land in the study area was divided into eight categories: vegetation-covered areas, offshore waters, cultured waters, paddy fields, other water bodies, swamps, construction land and unused land.

Geospatial Data Clean-Room Market Outlook

As per our latest research, the global geospatial data clean-room market size in 2024 is valued at USD 1.7 billion, driven by the escalating demand for privacy-centric data collaboration across industries. The market is projected to grow at a robust CAGR of 16.4% from 2025 to 2033, reaching an estimated USD 7.2 billion by the end of the forecast period. Growth is primarily attributed to increasing regulatory requirements for data privacy, the proliferation of location-based services, and the integration of geospatial data analytics in strategic decision-making processes across various sectors.

One of the most significant growth factors for the geospatial data clean-room market is the mounting pressure from data privacy regulations worldwide, such as GDPR in Europe and CCPA in California. As organizations increasingly rely on location-based data to drive insights, ensuring that data sharing and collaboration do not compromise privacy is paramount. Geospatial data clean-rooms provide a secure environment where sensitive information can be analyzed and integrated without exposing raw data, enabling organizations to unlock value while maintaining compliance. This capability is especially critical for industries such as healthcare, BFSI, and government, where the risks associated with data breaches are exceptionally high.

Another key driver is the rapid digital transformation and adoption of advanced analytics across sectors. Enterprises are leveraging geospatial intelligence to optimize supply chains, enhance customer experiences, and improve operational efficiency. The rise of cloud computing, IoT, and big data analytics has resulted in an exponential increase in geospatial data volumes. However, the challenge lies in harnessing this data collaboratively while mitigating risks associated with unauthorized access and data misuse. Clean-room solutions address these challenges by enabling privacy-preserving data integration, advanced analytics, and secure visualization, thereby fueling market expansion.

The growing sophistication of cyber threats and increasing incidents of data breaches have further underscored the necessity for robust data security frameworks. Organizations are prioritizing investments in geospatial data clean-room technologies to safeguard sensitive location data and ensure secure multi-party analytics. This trend is reinforced by the need to build trust with consumers and partners, as well as to comply with evolving industry standards. The convergence of artificial intelligence, machine learning, and geospatial analytics within secure clean-room environments is opening new avenues for innovation, driving both adoption and market growth.

Regionally, North America dominates the geospatial data clean-room market in 2024, accounting for over 38% of the global market share, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major technology players, robust regulatory frameworks, and high adoption rates of advanced analytics solutions. Europe is witnessing significant growth due to stringent data privacy regulations and increased investments in smart city initiatives. Meanwhile, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, digital transformation, and government-led geospatial data integration projects. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, driven by infrastructure development and increasing awareness of data privacy.

Component Analysis

The component segment of the geospatial data clean-room market is categorized into software, services, and hardware. Software forms the backbone of clean-room environments, providing the essential tools for secure data integration, privacy-preserving analytics, and visualization. The demand for advanced software solutions is being driven by the need for customizable, scalable, and interoperable platforms that can seamlessly integrate with existing enterprise systems. Vendors are focusing on incorporating artificial intelligence and machine learning capabilities into their offerings, enabling automated data classification, anomaly detection, and real-time analytics within the clean-room environment. As organizations seek to derive actionable insights from complex geospatial datasets, the software segment is expected to maintain its dominance thro

This dataset was derived from the primary ‘SLATS Landsat woody change data (25m) for 1988 – 2010’ raster (grid) layers used to generate the annualised woody vegetation change rates for the 2010 NSW Annual Report of Native Vegetation. This data describes the areas and type of woody vegetation\r change (loss) based on the analysis of multi-date Landsat imagery covering NSW.\r \r This data is based on a biennial LANDSAT coverage between 1988-2006 and annual coverage 2006-2010. LANDSAT Imagery 1988-2008 was processed by Geoscience Australia at 25m resolution.\r \r 2008 onwards is based on USGS processed LANDSAT at 30m resolution. Note, this vector data may generate slightly different aerial statistics to those generated from the source raster data. This is due to variation caused by the data transformation and vector cleaning processes applied in generating the vector data.

VDatum is designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels.This particular layer allows you to convert from NAVD 88 to MHHW.Units: metersThese data are a derived product of the NOAA VDatum tool and they extend the tool's Mean Higher High Water (MHHW) tidal datum conversion inland beyond its original extent.VDatum was designed to vertically transform geospatial data among a variety of tidal, orthometric and ellipsoidal vertical datums - allowing users to convert their data from different horizontal/vertical references into a common system and enabling the fusion of diverse geospatial data in desired reference levels (https://vdatum.noaa.gov/). However, VDatum's conversion extent does not completely cover tidally-influenced areas along the coast. For more information on why VDatum does not provide tidal datums inland, see https://vdatum.noaa.gov/docs/faqs.html.Because of the extent limitation and since most inundation mapping activities use a tidal datum as the reference zero (i.e., 1 meter of sea level rise on top of Mean Higher High Water), the NOAA Office for Coastal Management created this dataset for the purpose of extending the MHHW tidal datum beyond the areas covered by VDatum. The data do not replace VDatum, nor do they supersede the valid datum transformations VDatum provides. However, the data are based on VDatum's underlying transformation data and do provide an approximation of MHHW where VDatum does not provide one. In addition, the data are in a GIS-friendly format and represent MHHW in NAVD88, which is the vertical datum by which most topographic data are referenced.Data are in the UTM NAD83 projection. Horizontal resolution varies by VDatum region, but is either 50m or 100m. Data are vertically referenced to NAVD88 meters.More information about the NOAA VDatum transformation and associated tools can be found here.

We study the effect of expanding trade on societal coalitions through its impact on development. We combine a majoritarian political model with a spatial model of trade to argue that trade-induced economic change---by bringing new workers to locations closer to world markets---can lead to losses rather than gains in political power for the factors of production advantaged by increased trade. We study how this phenomenon explains rising protectionism in the US from 1880 to 1900. Using county-level changes in transportation costs induced by railroad expansion, our estimates indicate that falling costs increased population and farm values but reduced the proportion of farmers. Reduced transportation costs caused a reduction in vote shares for the Democratic Party, which favored liberal trade policies, and an increase in an original newspaper-based measure of protectionist sentiment. Expanding trade alters not only political interests but also the geographic distribution of those interests.

Biogeoclimatic Ecosystem Classification (BEC) system is the ecosystem classification adopted in the forest management within British Columbia based on vegetation, soil, and climate characteristics whereas Site Series is the smallest unit of the system. The Ministry of Forests, Lands, Natural Resource Operations and Rural Development held under the Government of British Columbia (“the Ministry”) developed a web-based tool known as BEC Map for maintaining and sharing the information of the BEC system, but the Site Series information was not included in the tool due to its quantity and complexity. In order to allow users to explore and interact with the information, this project aimed to develop a web-based tool with high data quality and flexibility to users for the Site Series classes using the “Shiny” and “Leaflet” packages in R. The project started with data classification and pre-processing of the raster images and attribute tables through identification of client requirements, spatial database design and data cleaning. After data transformation was conducted, spatial relationships among these data were developed for code development. The code development included the setting-up of web map and interactive tools for facilitating user friendliness and flexibility. The codes were further tested and enhanced to meet the requirements of the Ministry. The web-based tool provided an efficient and effective platform to present the complicated Site Series features with the use of Web Mapping System (WMS) in map rendering. Four interactive tools were developed to allow users to examine and interact with the information. The study also found that the mode filter performed well in data preservation and noise minimization but suffered from long processing time and creation of tiny sliver polygons.

Geographic Information System Software Market Outlook

The global Geographic Information System (GIS) Software market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach USD 15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This impressive growth can be attributed to the increasing demand for efficient data management tools across various industries, which rely on spatial data for decision-making and strategic planning. The rapid advancements in technology, such as the integration of AI and IoT with GIS software, have further propelled the market, enabling organizations to harness the full potential of geographic data in innovative ways.

One of the primary growth drivers of the GIS Software market is the burgeoning need for urban planning and smart city initiatives worldwide. As urbanization trends escalate, cities are increasingly relying on GIS technology to manage resources more effectively, optimize transportation networks, and enhance public safety. The ability of GIS software to provide real-time data and spatial analysis is vital for city planners and administrators faced with the challenges of modern urban environments. Furthermore, the trend towards digital transformation in governmental organizations is boosting the adoption of GIS solutions, as they seek to improve operational efficiency and service delivery.

The agricultural sector is also experiencing significant transformations due to the integration of GIS software, which is another pivotal growth factor for the market. Precision agriculture, which involves the use of GIS technologies to monitor and manage farming practices, is enabling farmers to increase crop yields while reducing resource consumption. By leveraging spatial data, farmers can make informed decisions about planting, irrigation, and harvesting, ultimately leading to more sustainable agricultural practices. This trend is particularly prominent in regions where agriculture forms a substantial portion of the economy, encouraging the adoption of advanced GIS tools to maintain competitive advantage.

Another influential factor contributing to the growth of the GIS Software market is the increasing importance of environmental management and disaster response. GIS technology plays a crucial role in assessing environmental changes, managing natural resources, and planning responses to natural disasters. The ability to overlay various data sets onto geographic maps allows for better analysis and understanding of environmental phenomena, making GIS indispensable in tackling issues such as climate change and resource depletion. Moreover, governments and organizations are investing heavily in GIS tools that aid in disaster preparedness and response, ensuring timely and effective action during emergencies.

The evolution of GIS Mapping Software has been instrumental in transforming how spatial data is utilized across various sectors. These software solutions offer robust tools for visualizing, analyzing, and interpreting geographic data, enabling users to make informed decisions based on spatial insights. With the ability to integrate multiple data sources, GIS Mapping Software provides a comprehensive platform for conducting spatial analysis, which is crucial for applications ranging from urban planning to environmental management. As technology continues to advance, the capabilities of GIS Mapping Software are expanding, offering more sophisticated features such as 3D visualization and real-time data processing. These advancements are not only enhancing the utility of GIS tools but also making them more accessible to a wider range of users, thereby driving their adoption across different industries.

Regionally, North America and Europe have traditionally dominated the GIS Software market, thanks to their robust technological infrastructure and higher adoption rates of advanced technologies. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on infrastructure development, and the expanding telecommunications sector. The growing awareness and adoption of GIS solutions in countries like China and India are significant contributors to this regional growth. Furthermore, Latin America and the Middle East & Africa regions are slowly catching up, with ongoing investments in smart city projects and infrastructure development driving the demand for GIS software.

Component Analysis</h2&

The evolutionary model of construction land serves as a fundamental pillar in national spatial development and planning research. However, previous studies have overlooked the "climbing" mode of construction land on three-dimensional terrains. To address this issue, utilizing elevation data and land use data from 2010 to 2020, this study employs slope analysis, intensity analysis, spatio-temporal transformation, and PLUS model to elucidate the spatial expansion process and driving forces of urban construction land in Chongqing from both two-dimensional and three-dimensional perspectives. The findings indicate that: (1) From a three-dimensional topographical standpoint, between 2010 and 2012, construction land gradually expanded towards low-slope areas, whereas between 2012 and 2020, it progressively extended into high-slope regions. (2) Regarding land type conversion patterns, the shift from arable land to construction land demonstrates a systematic inclination, while other transformations exhibit absolute or relative tendencies. Conversely, the conversion from construction land to arable land also displays a systematic pattern. (3) Since 2010, the growth process of construction land has transitioned from slow-equilibrium to rapid-disequilibrium with an expanding spatial disparity. (4) Most areas maintain relatively stable spatial conditions without significant jumps or transitions observed. (5) The expansion of construction land in Chongqing is primarily influenced by terrain, river, tunnel, rail transit, and other factors. The outcomes of this study can provide scientific foundations and decision-making references for rational planning in similar cities characterized by mountainous landscapes intersected by rivers.

Human use of oceans has dramatically increased in the 21st century. Sea turtles are vulnerable to anthropogenic stressors in the marine environment because of lengthy migrations between foraging and breeding sites, often along coastal migration corridors. Little is known about how movement and threat interact specifically for male sea turtles. To better understand male sea turtle movement, and the threats they encounter, we satellite-tagged 40 adult male sea turtles of four different species. We calculated movement patterns using state-space modeling (SSM), and quantified threats in seven unique categories; shipping, fishing, light pollution, oil rigs, proximity to coast, marine protected area (MPA) status, and location within or outside of the U.S. Exclusive Economic Zone (EEZ). We found significantly higher threat severity in northern and southern latitudes for green turtles (Chelonia mydas) and Kemp’s ridleys (Lepidochelys kempii) in our study area. Those threats were pervasive, with..., 2.1 Study Area/Species Collection We captured turtles as in Hart et al. [100] from 2009 – 2019. Forty adult male sea turtles of four different species were captured from four locations using a boat (jumping from a boat, snorkeling) or net capture via trawler. Sample sizes are as follows: Kemp’s ridley = 6, hawksbill = 1, loggerhead = 8, green = 25. Capture location sample sizes are as follows: Dry Tortugas National Park = 24, Florida Keys National Marine Sanctuary = 6, Northern Gulf of Mexico = 9, Buck Island National Reef Monument = 1. We followed standard morphometric data collection, and attached platform transmitter terminals (PTT) to each turtle carapace using slow-curing epoxy (two-part Superbond epoxy; see Hart et al. [100]). Turtles were tracked using Wildlife Computers (Redland, Washington, U.S.A.) SPOT or SPLASH10 transmitters. Tracking data ranged from 8 June 2009 to 7 August 2020 [100, 101]. 2.2 Collection and Calculation of Threats/State-Space Modelling We performed a switc..., Please see the attached metadata release for navigating our dataset. Attached is all the transformed data, locations where raw data was accessed, and code for figures and statistical analysis. Coordinates have been removed to protect sensitive species locations.Therefore some of these data transformations in the code cannot be run. Requests for coordinate data can be made to Dr. Kristen Hart: kristen_hart@usgs.gov, # Data from: one shell of a problem: cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents

https://doi.org/10.5061/dryad.1rn8pk0ww

Description of the data and file structure

These data are for Ashford et al. 2022

This data release contains the necessary R code to reproduce the data and results from this experiment. Data can be found at the USGS public data release ttps://doi.org/10.5066/P958OAKJ. Please be aware that because these data contain the locations of

sensitive species, coordinates have been removed which mean some of the code cannot be run.

Please see the following for the contents of each folder:

1. Code for Predictions and Figures:

a. Density and Threat: The R code that was used to test the assumption that sea turtle density could predict threat.

b. Figure 2 R Code: Contains the R code necessary to recreate figure 2 in the paper.

c. Int...

NOTE: this version, V2B, has been REPLACED by reference 2294375: https://irma.nps.gov/DataStore/Reference/Profile/2294375. The 2294375 replacement was derived using newer methods where outputs were generated using Google Earth Engine (GEE) instead of IDL and following a newer protocol: Protocol for Landsat-based monitoring of landscape change in North Coast and Cascades Network parks: Version 2.1, reference code 2294109. Landsat/LandTrendr derived landscape change data from Olympic National Park and surrounding study area, labeled by landscape change type chosen from a discrete list. This data set is an updated version (V2B) of the data set summarized in the following report: Landsat-based Monitoring of Landscape Dynamics in Olympic National Park: 1985-2010. Natural Resource Data Series NPS/NCCN/NRDS—2016/1053. The updates include the addition of Confidence and Alt_type fields and additional office validation and labeling of patches inside the park boundary and surrounding USFS Wilderness Areas. This data set is considered superior to the V2A data set that was used for report summaries. As part of Vital Signs Monitoring, the North Coast and Cascades Network (NCCN) of the National Park Service (NPS) developed a protocol for monitoring landscape change using Landsat satellite imagery. The protocol was implemented at Olympic National Park (OLYM) in 2014 using LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithms developed by Oregon State University. LandTrendr tracks the spectral trajectory of Landsat pixels through time and smoothes their spectral index signatures into coherent segments describing periods of stability or change. The primary outputs from LandTrendr are the year of change onset, the duration of change, and the magnitude of the change. Adjacent pixels with the same year of change onset are then grouped into patches. Only changes larger than 0.8 ha (2 ac) and for which the duration of the period of landscape change is less than or equal to 4 years are retained. Nine categories of landscape change were mapped: Avalanches, Clearing, Development, Fire, Mass Movements, Progressive Defoliation, Riparian, Tree Topplings, and Winter Ice. The Avalanche category captures long, linear change which partially or completely removes vegetation from the valley wall following a release of a large mass of snow down a mountain side. Clearings are areas under forest management where practices vary from thinning to clearcuts. The Development category captures changes associated with complete and persistent removal of vegetation and transformation to a built landscape. Changes due to Fire vary in intensity from full canopy removal to partial burns that leave behind a mixture of dead and singed trees. The Mass Movement category includes both landslides found on valley walls and debris flows associated with streams. Progressive Defoliation is a change type in which the forest cover remains but has declined due to insect infestation, disease or drought. Riparian changes are restricted to the valley floors alongside major streams and rivers and capture areas where either conifer or broadleaf vegetation previously existed and has been converted to river channel. Change due to Tree Toppling is evidenced by broken or topped trees, generally due to wind but sometimes to root rot. Winter Ice category captures changes in vegetation damaged by heavy, long lasting snow and ice followed by sever winds; generally characterized by broken tree branches. These data were summarized in Copass, C., N. Antonova, and S. Clary. 2016. Landsat-based monitoring of landscape dynamics in Olympic National Park: 1985-2010. Natural Resource Data Series. NPS/NCCN/NRDS—2016/1053. National Park Service. Fort Collins, Colorado (https://irma.nps.gov/DataStore/Reference/Profile/2233073).

The geospatial analytics market, valued at $93.91 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 18.68% from 2025 to 2033. This significant expansion is fueled by several key factors. The increasing adoption of advanced technologies like GPS, GIS, and remote sensing across diverse sectors is a major driver. The BFSI (Banking, Financial Services, and Insurance), government and utilities, and telecom industries are particularly heavy users, leveraging geospatial analytics for improved risk assessment, resource management, and customer service. Furthermore, the rising demand for precise location-based services in manufacturing, automotive, and retail sectors is contributing to market growth. The integration of AI and machine learning into geospatial analytics platforms enhances analytical capabilities, further driving adoption. Government initiatives promoting digital transformation and smart city projects also significantly boost market demand. While data privacy and security concerns represent a potential restraint, the overall market outlook remains highly positive due to the expanding applications of geospatial analytics across various sectors and geographical regions. North America currently holds a dominant market share, primarily driven by the presence of major technology companies and significant investments in infrastructure development. However, the Asia-Pacific region is poised for rapid growth, fueled by increasing urbanization, rising digital adoption, and government investments in infrastructure development in countries like China and India. Europe also contributes significantly to market revenue, with strong growth expected in several key countries. The competitive landscape is characterized by a mix of large multinational corporations and specialized technology providers. Key players are focusing on strategic partnerships, acquisitions, and technological innovations to enhance their market position and cater to evolving customer demands. The market is expected to witness increased consolidation in the coming years as companies strive to expand their product offerings and geographical reach. The overall market dynamics indicate a bright future for geospatial analytics, with continued innovation and growth anticipated across all segments.