Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.Geospatial Solutions Market: Definition/ OverviewGeospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

During 2005−2010, we investigated interactions between brown bears (n=19) and wolves (n=7) by means of GPS-telemetry in a long-established national park in the central Apennines, Italy, where bears and wolves always coexisted close to humans. Based on K-select analysis and a randomization approach we assessed the extent of overlap between the species' niches on a seasonal basis. In additon, we used multi-species Resource Selection Functions (RSFs) at the 3rd-order selection (i.e., home range level) to investigate their habitat choice taking into account an intraguild predictor (i.e., the probability of occurrence n te other species). Bears and wolves clearly segregated in fall but not during summer, when the overlap between their realized niches suggests a convergent adaptation to a seasonal peak of anthropogenic pressure. Using multi-species RSFs, however, habitat choice by bears and wolves differed also when their niches overlapped, and their probability of occurrence was reciprocally..., The dataset refers to environmental and anthropogenic variables, derived in a GIS environment, in points (i.e., locations) used ("Pres"=1) and available ("Pres"=0) to sympatric adult bears (Ursus arctos marsicanus, n=19) and wolves (Canis lupus, n=7) living in the Abruzzo Lazio and Molise National park, central Italy, from 2005 to 2010. Animal locations ("Pres"=1) were recorded by means of Global Positionin System (GPS) collars, whereas available points ("Pres"=0) were randomly selected at a density of 10 locations/km2 within ther seasonal home range to prepresent resources available. GPS locations were recorded at 3-hr intervals, but their coordinates are omitted from the files due to serurity reason (Apennine brown bears are Critically Endandered according to the IUCN Regional Red List Criteria). The dataset is organized in 8 diferent Excel files, one for each species and season, as indicated by their names. The data have been used to conduct (1) K-select seasonal analyses to assess n..., , # Niche overlap and habitat selection by sympatric bears and wolves

https://doi.org/10.5061/dryad.cjsxksndn

Environmental and anthropogenic variables associated with bears' and wolves' Global Positioning System locations in the central Apennines, Italy (2005-2010).

Description of the data and file structure

The dataset refer to environmental and anthropogenic variables recorded in a GIS environment on points (i.e., locations) used ("Pres"=1) and available ("Pres=0) to sympatric adult bears (Ursus arctos marsicanus, n=16) and wolves (Canis lupus, n=7) living in the Abruzzo Lazio and Molise National park, central Italy, from 2005 to 2010.

Animal locations ("Pres"=1) were recorded by means of Global Positioning System (GPS) collars, whereas available points ("Pres"=0) were randomly selected at a density of 10 locations/km2 within their seasonal home range to prepresent resources available.

GPS locations were recorded at 3-hr intervals, bu...

GIS Layers used to create the hunting habitat model, which include Cattle density, Distance from edge, Dominant landcover, Forest edge density, Forest patch size, Improved pasture patch size, Landcover, and Percent forest cover. Area of analysis defined by Minimum Convex Polygons created from Florida panther GPS data.

A GPS survey by Andrew Ruddell (AAD Glaciology) on 9 March 1999 at Casey Station, Antarctica. The survey was conducted along the road from Casey Station to Old Casey. The aim of the survey was to investigate the cause of the 'disappearance' of road gravel applied to the compacted snow road in the depression between Casey Station to Old Casey. This dataset consists of point data with an elevation (above mean sea level) attribute. The data, in Excel and shapefile formats, and Andrew's report are available for download (see Related URL below).