Difference between revisions of "Spatial Analysis"
| Line 5: | Line 5: | ||
==Interactive Spatial Analysis Tools== | ==Interactive Spatial Analysis Tools== | ||
Historically, spatial analysis tools have been challenging to code in a reproducible way, due to proprietary software systems with little extensibility. ArcGIS is a prime example of this, as the default implementation can only be coded reproducibly using [https://libraries.mit.edu/files/gis/PythonProgrammingforArcgis.pdf Python]. The open-source equivalent [http://qgis.org/en/site/ QGIS] has [http://www.qgistutorials.com/en/docs/getting_started_with_pyqgis.html similar limitations]. Both are useful for exploring spatial data | Historically, spatial analysis tools have been challenging to code in a reproducible way, due to proprietary software systems with little extensibility. ArcGIS is a prime example of this, as the default implementation can only be coded reproducibly using [https://libraries.mit.edu/files/gis/PythonProgrammingforArcgis.pdf Python]. The open-source equivalent [http://qgis.org/en/site/ QGIS] has [http://www.qgistutorials.com/en/docs/getting_started_with_pyqgis.html similar limitations]. Both are useful for exploring spatial data interactively, but poor for saving and reproducing output workflows. | ||
==Reproducible Coding for Spatial Analysis== | ==Reproducible Coding for Spatial Analysis== | ||
By contrast, programs such as [https://www.r-project.org R] now support an [http://www.rspatial.org/index.html extensive suite] of open-source tools for the reproducible manipulation of spatial datasets. These tools are essential for tasks that involve creating new data – especially when specific formulae or measurements are needed or parameters may want to be varied in subsequent runs. | |||
[https://www.stata.com Stata] also supports some [https://www.stata.com/support/faqs/graphics/spmap-and-maps/ commands like spmap] for spatial analysis, such as displaying maps with data. However it is not well-equipped to calculate new statistics, such as linking points to underlying political geographies or surrounding features, as it handles non-point information poorly in general. | |||
==Cloud Computing for Spatial Analysis== | ==Cloud Computing for Spatial Analysis== | ||
==Resources== | ==Resources== | ||
Revision as of 15:43, 9 November 2017
Introduction
Spatial analysis involves utilizing geographical information to create descriptive and informative outputs. These can be maps or other images and/or statistical data such as distances to other points, boundaries, roads or public services. The development world is rapidly expanding its capacity to access and analyze spatial data ranging from market clustering to satellite imagery to agricultural plot mapping in SurveyCTO, the ability to process this data is also improving quickly.
Interactive Spatial Analysis Tools
Historically, spatial analysis tools have been challenging to code in a reproducible way, due to proprietary software systems with little extensibility. ArcGIS is a prime example of this, as the default implementation can only be coded reproducibly using Python. The open-source equivalent QGIS has similar limitations. Both are useful for exploring spatial data interactively, but poor for saving and reproducing output workflows.
Reproducible Coding for Spatial Analysis
By contrast, programs such as R now support an extensive suite of open-source tools for the reproducible manipulation of spatial datasets. These tools are essential for tasks that involve creating new data – especially when specific formulae or measurements are needed or parameters may want to be varied in subsequent runs.
Stata also supports some commands like spmap for spatial analysis, such as displaying maps with data. However it is not well-equipped to calculate new statistics, such as linking points to underlying political geographies or surrounding features, as it handles non-point information poorly in general.