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About 10,000 papers for “Geospatial analysis”

Geospatial Analysis

Shashi Shekhar, Hui Xiong, Xun Zhou · Encyclopedia of GIS · 2017

Cited by 0

Geospatial Analysis Framework

Elisabeta Antonia Haller · SHILAP Revista de lepidopterología · 2010

In a computerized society, the volume of data grows unexpectedly, making their processing time a very difficult task. A priority has become the processing of data in useful information and knowledge. Thus we can say that data mining is a result of technological developments. Interpretation of spatial data has made the subject of research over time, reaching now to have a large variety of instruments and software products for representation and interpretation. What we need to understand beyond the facilities offered by one system or another, proprietary or open source solution, is how they work and interact with spatial data.<br />

Cited by 1Open access

Geospatial Data Analysis

Tewodros Demise Bezabih, Mohammad Gouse Glaety, Desalegn Aweke Wako et al. · Advances in geospatial technologies book series · 2024

This chapter examines the introduction to geospatial data, Python programming, and the implications of Python in geospatial data analysis. There are different forms of geospatial data: tabular data, raster data, and vector data. The package or library is essential when using geospatial data in Python programming. Geospatial data is huge and complex because of the nature of geospatial data computing Python is used for. This chapter briefly introduced the Role of Python libraries in Geospatial Analysis such as Arcpy, Basemap, Cartopy, EarthPy, Fiona-GO, Folium, GDAL and OGR, GEE-Py, GeoAlchemy, Geocoder, Geodaisy, Geopandas, Geoplot, Geopy, Geopyspark, GeospatialPDF, GeostatsPy, GPSBabel, 3-Py,ipyleaflet, KeplerglPandas, Plotl, Plotly Express, Plotnine, PyGeos, SentinelHub-Py, Shapely, SpatialPandas, Turfpy. Python has emerged as an indispensable tool in geospatial data analysis.

Cited by 5Open access

and geospatial analysis

In-young Yeo, Chengquan Huang, Doi /s et al. · 2011

1 23Your article is protected by copyright and all rights are held exclusively by Springer-Verlag. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your work, please use the accepted author’s version for posting to your own website or your institution’s repository. You may further deposit the accepted author’s version on a funder’s repository at a funder’s request, provided it is not made publicly available until 12 months after publication.

Cited by 0

Cloud-based Geospatial Analysis

Girgin, Serkan · Zenodo (CERN European Organization for Nuclear Research) · 2021

Recent development in both hardware and software infrastructure have given big push and new direction to geospatial data processing capabilities. Scalable and afforable geospatial data analysis available through a) open-source systems that allow computing clusters on commodity hardware, b) proprietary cloud-based data storage and computing services. However, it is challenging to choose the right solution(s) depending on the nature of geospatial (big) data and the analysis needs. This presentation discuss the needs and provides information on existing cloud computing service and platforms, which can serve these needs. Main characteristics and potential benefits of cloud computing are described, and suggestions are provided for better use of cloud resources to move research to the cloud.

Cited by 0Open access

Worldwide geospatial risk analysis.

Krzysztof Kiryluk (156863), Yifu Li (156864), Simone Sanna-Cherchi (156865) et al. · Figshare · 2015

<p>Surface interpolation of the standardized risk score over Africa and Euroasia (main), and Americas (inset). Symbols represent the locations of sampled populations: HGDP (circles), HapMap-III (diamonds), and healthy controls from this study (triangles).</p>

Cited by 0Open access

Cloud-based Geospatial Analysis

Girgin, Serkan · Zenodo (CERN European Organization for Nuclear Research) · 2021

Recent development in both hardware and software infrastructure have given big push and new direction to geospatial data processing capabilities. Scalable and afforable geospatial data analysis available through a) open-source systems that allow computing clusters on commodity hardware, b) proprietary cloud-based data storage and computing services. However, it is challenging to choose the right solution(s) depending on the nature of geospatial (big) data and the analysis needs. This presentation discuss the needs and provides information on existing cloud computing service and platforms, which can serve these needs. Main characteristics and potential benefits of cloud computing are described, and suggestions are provided for better use of cloud resources to move research to the cloud.

Cited by 0Open access

Details of geospatial analysis.

Marissa E. Renardy (14670558), Craig Gillen (14670561), Zhenhua Yang (150675) et al. · Figshare · 2023

<div><p>Tuberculosis (TB) elimination in the United States remains elusive, and community-specific, localized intervention strategies may be necessary to meet elimination goals. A better understanding of the genotypic diversity of Mtb, the population subgroups affected by different TB strains, and differences in disease presentation associated with these strains can aid in identifying risk groups and designing tailored interventions. We analyze TB incidence and genotype data from all Arkansas counties over an 11-year time span from 2010 through 2020. We use statistical methods and geographic information systems (GIS) to identify demographic and disease phenotypic characteristics that are associated with different Mtb genetic lineages in the study area. We found the following variables to be significantly associated with genetic lineage (p<0.05): patient county, patient birth country, patient ethnicity, race, IGRA result, disease site, chest X-ray result, whether or not a case was identified as part of a cluster, patient age, occupation risk, and date arrived in the US. Different Mtb lineages affect different subpopulations in Arkansas. Lineage 4 (EuroAmerican) and Lineage 2 (East Asian) are most prevalent, although the spatial distributions differ substantially, and lineage 2 (East Asian) is more frequently associated with case clusters. The Marshallese remain a particularly high-risk group for TB in Arkansas.</p></div>

Cited by 0Open access

Synthetic geospatial data for performance analysis of geospatial database systems

Paolo Garza · Zenodo (CERN European Organization for Nuclear Research) · 2017

This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosaptial datasbases. The datasets is composed of four json file, characterized by different size. They can be used to analyze the scalability of geospatial datasets with respect to the database size. Each json file contains a set of "points", each one characterized by a set of random attributes (description, url of a picture linked to the point, creation date, delete date, update date, identifier, partition identifier). The synthetically generated points are uniformly distributed among the world.

Cited by 0Open access

A Geospatial Data and Model Hub for Online Geospatial Analysis

Haipeng Deng, Peng Yue, Dayu Yu et al. · 2023

In recent years, the availability of online geospatial analysis tools has increased, allowing users to access and manipulate large geospatial datasets and perform complex analyses through Web-based interfaces. It is still a challenge on how to manage geospatial resources including data and analysis functions in a distributed infrastructure. To address this issue, the study proposes a geospatial data and model hub approach for online geospatial analysis. Two core components, named the Data Center and the Model Center, are involved. The Data Center serves as a data hub for integrating, storing, and sharing geospatial datasets and metadata. It employs Cloud-optimized GeoTIFF (COG) and GeoCube for efficient handling of cloud-based data organization. The Model Center as a model hub focuses on the registration and sharing of geospatial analysis models, facilitating collaboration and interoperability among models. Both components are integrated into an online cloud-based geoprocessing platform, named the Open Geospatial Engine (OGE). The applicability is demonstrated through a case study involving the discovery, access, and manipulation of large datasets and models.

Cited by 4