ArcGIS Desktop

ArcGIS helps you use spatial information to perform deep analysis, gain a greater understanding of your data, and make more informed decisions. It’s a platform for: Professional GIS enables you to design and manage solutions through the application of geographic knowledge; Location Analytics provides data visualization and geographic intelligence for business analytics systems. Use ArcGIS for Developers to quickly add geo to your apps using Esri cloud services. Use the Esri Developer Network to get a developer license for all the software in the ArcGIS system.

References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Martijn Tennekes: tmap: Thematic Maps in R (2018)
  2. McCoy, Dana Charles; Connors, Maia C.; Morris, Pamela A.; Yoshikawa, Hirokazu; Friedman-Krauss, Allison H.: Neighborhood economic disadvantage and children’s cognitive and social-emotional development: exploring head start classroom quality as a mediating mechanism (2015) MathEduc
  3. Quinn Payton; Michael McManus; Marc Weber; Anthony Olsen; Thomas Kincaid: micromap: A Package for Linked Micromaps (2015)
  4. Ver Hoef, Jay M.; Jansen, John K.: Estimating abundance from counts in large data sets of irregularly spaced plots using spatial basis functions (2015)
  5. Erin Peterson; Jay Ver Hoef: STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data (2014)
  6. Thibault Laurent; Anne Ruiz-Gazen; Christine Thomas-Agnan: GeoXp: An R Package for Exploratory Spatial Data Analysis (2012)
  7. Bozkaya, Burcin; Yanik, Seda; Balcisoy, Selim: A GIS-based optimization framework for competitive multi-facility location-routing problem (2010)
  8. Michaelis, Christopher D.; Ames, Daniel P.: Evaluation and implementation of the OGC web processing service for use in client-side GIS (2009) ioport
  9. Hill, Jason; Hossain, Faisal; Sivakumar, Bellie: Is correlation dimension a reliable proxy for the number of dominant influencing variables for modeling risk of arsenic contamination in groundwater? (2008)