Spatial Analysis with QGIS - Part I: Point Data

QGIS 2.8 Wien was released, so it is a good time to review QGIS's basic spatial analysis capabilities for vector data--starting with point data. We will also take a look at a few plugins and the SAGA and R processing toolboxes. Most of the functionality in QGIS is from Ftools, formerly a plugin, now part of base QGIS. There is also the MMQGIS plugin to examine vector data.

In addition, I will make a few recommendations for added features, or point you to another free or open source program that can be used in conjunction with QGIS or simply by importing and exporting data.

Nearest Neighbor Index

QGIS can calculate the nearest neighbor index to assess point clustering.  No p-value is given but the simple trick is to remember that large negative z-scores mean the points are clustered while large positive z-scores mean the data is more dispersed.

No p-values are given but remembering critical values/decision points,i.e. +/-1.65, 1.96,
is the easiest way to know if clustering is statistically significant.

Mean Center and Standard Distance
The mean center, an average of x- and y- coordinates, is an easy way to find the central feature and to examine spatial-temporal trends.  In the case below, the mean of all starting points, by year, for US tornadoes, 2000-2013. The data are grouped by UID, in this case a year variable.  It would be great to also be able to calculate a median center.  Data source: NOAA Storm Prediction Center.

• In some years, the average was pulled slightly west or east.  Interestingly, the mean is pulled east in 2011, when there was a large 'outbreak' of tornadoes across the southeastern US.

The mean of all 'starting' points for US tornadoes, by year, 2000-2013.

Moreover, there are several point pattern analysis tools, including the standard distance--a measure of dispersion--in the SAGA Processing Toolbox.  More specifically the "Geostatistics" tool, contains a lot of useful functions that can be used.  The output can be  saved and displayed in QGIS.  The NOAA dataset already contains the length from start to end, but you could also calculate this by creating a distance matrix in QGIS.

The SAGA Geostatistics Toolbox in QGIS

Ripley's K
Ripley's K helps to determine clustering at different distances.  It can be implemented through the R processing toolbox in QGIS, using R's SpatStat package, or CrimeStat.

Heatmap
You can download the Heatmap plugin or use a built-in live/dynamic heat map when you go to style a layer.  For the latter, make sure to move the rendering slider to 'best' for a nice looking heatmap. Here is an example using the dynamic heat map to look at homicides in Philadelphia. Data source: OpenDataPhilly.   In future posts, we will also look at alternatives to heatmaps, like gridding/quadrat analysis.

QGIS has lot of neat options for styling vector data, including a dynamic heatmap
that changes as you zoom in and out.

 (Note: In ArcGIS kernel density tool (not to be confused with point density) remains separate from the base software and has to be purchased through the Spatial Analyst Extension).

Grouping Analysis
Lastly, grouping analysis can be examined using PostGIS, which allows for a wide variety of spatial queries using SQL, or CrimeStat.

Near future...
We will look at spatial analysis of line and polygon data as well joining points for analysis.

GME and ArcGIS
When using ArcGIS, be sure to check out the free windows-based program Geospatial Modelling Environment, or GME formerly 'Hawth's Tools," http://www.spatialecology.com/gme/.  GME has a long list of helpeful commands: http://www.spatialecology.com/gme/gmecommands.htm.

CrimeStat & GME vs. ArcGIS: Kernel Density

Many spatial analyses begin with using kernel density in GIS.  In ArcGIS, kernel density is part of the Spatial Analyst Extension.  However, several viable alternatives exist.  For today's post, I chose two of the easiest to implement and the ones that I have had the most success with: CrimeStat and Geospatial Modeling Environment (GME), formerly known as Hawth's Tools. Note: For GME you will also have to have R installed and several spatial packages.  They are both free, so enjoy!

When using these different tools, keep in mind that there are different kernel functions. ArcGIS uses a quadratic estimation while CrimeStat and GME have several. Click on the image below to magnify it.   The maps show density analysis of Wifi spots in New York City.

I chose different kernel functions to highlight the intricacies of density analysis.  In addition, ESRI has a video on performing proper density analysis, which you should check out.

Crimestat is a lightweight program that is relatively straightforward.  GME requires more installation steps but uses a point-and-click interface to generate the density map. After installing GME and R, in GME, be sure to search and use r.setpath to link GME to R. In addition, in GME you can copy, paste, and edit code in the same window--an extremely helpful feature!

Notes: I have been rather frustrated with the kernel density implementation in GRASS and Quantum GIS--even after diving into the help pages and discussion boards.