http://www.bing.com:80/search?q=Spatial+Data+ApplicationsSearch resultshttp://www.bing.com:80/s/a/rsslogo.gifhttp://www.bing.com:80/search?q=Spatial+Data+ApplicationsCopyright © 2026 Microsoft. All rights reserved. These XML results may not be used, reproduced or transmitted in any manner or for any purpose other than rendering Bing results within an RSS aggregator for your personal, non-commercial use. Any other use of these results requires express written permission from Microsoft Corporation. By accessing this web page or using these results in any manner whatsoever, you agree to be bound by the foregoing restrictions.https://link.springer.com/article/10.1007/s11222-025-10688-6Interval-valued data, characterized by intrinsic measurement imprecision, uncertainty, and variability, are common in real-world applications. This study introduces a novel spatial autoregressive model tailored for interval-valued data, unifying and generalizing several existing frameworks. To address the limitations of interval representations, we develop a joint quasi-maximum likelihood ...Mon, 06 Apr 2026 01:45:00 GMThttps://www.sciencedirect.com/science/chapter/bookseries/pii/B9780443236655000259Water resource mapping, monitoring, and modeling represent a multidisciplinary approach leveraging geospatial technologies to analyze and manage water resources comprehensively. This chapter provides an overview of methodologies, technologies, and applications involved in water resource assessment using geographic information systems (GIS), remote sensing (RS), hydrological modeling, and ...Thu, 26 Mar 2026 06:08:00 GMThttps://www.sciencedirect.com/science/article/pii/S1574954124001766In the selected studies, the most appropriate spatial method depended on the specific characteristics of the study. Using explicit spatial covariates and data splitting for training-testing provided more insights into a method's applicability. We summarize these insights and provide considerations for selecting the most appropriate method.Sat, 04 Apr 2026 03:13:00 GMThttps://link.springer.com/chapter/10.1007/978-3-031-53092-0_7In recent years, spatiotemporal data has continued to proliferate with the development of data collecting technologies such as the Global Positioning System (GPS), the Internet of Things (IoT), advanced sensors, cameras, loop detectors, and various mobile applications, including social media. Efficient and effective analysis of spatiotemporal data can help extract crucial information in ...Wed, 08 Apr 2026 08:16:00 GMThttps://opensourcegisdata.com/geospatial-data-understanding-collection-and-applications.htmlUnlocking the secrets of our world through the lens of geospatial data, comprehensive guide to collection, types, and applications in shaping a better future.Mon, 06 Apr 2026 00:04:00 GMThttps://www.sciencedirect.com/science/article/pii/S1569843225000159The fairness aspect is also highly relevant to spatial data and GeoAI, as ML models are increasingly used for map generation and spatial prediction in critical applications including agriculture, carbon budgeting, water management, economic development monitoring, disaster response, etc.Wed, 08 Apr 2026 03:30:00 GMThttps://www.safegraph.com/guides/uses-of-geospatial-data/To demonstrate further, we’ll look at 10 popular uses of geospatial data and some of the types of data that power them. Top 10 uses of geospatial data & where to get the data you need Geospatial data is often used in scientific or government administration contexts, but it has an increasing number of commercial uses as well.Tue, 07 Apr 2026 05:47:00 GMThttps://www.sciencedirect.com/science/article/pii/S0957417415002407The main contribution of this paper is a framework for clustering time-series and spatial data based on topological properties, which can correctly identify qualitative aspects of a data set currently missed by traditional distance-based techniques.Fri, 20 Mar 2026 02:48:00 GMThttps://blog.gramener.com/spatial-analysis-examples/What is Spatial Analysis? Spatial analysis is the process of examining attributes, locations, and relationships between features of spatial data. It uses analytics, computational models, and algorithms to address a pain point or mine useful knowledge. Spatial analysis generates actionable insights of value from spatial data. It has many applications, including emergency management, urban ...Sat, 04 Apr 2026 18:01:00 GMThttps://www.tandfonline.com/doi/full/10.1080/19475683.2024.24197492. Geospatial big data Geospatial big data refers to massive datasets with spatial or geographical components, collected from diverse sources. These data capture dynamic spatial phenomena, representing the locations, movements, and activities of people, objects, or natural systems over time. The emergence of new technologies, such as smart mobile devices, various satellites, drones, and ...Sat, 04 Apr 2026 06:19:00 GMT