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 IndexQGIS 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.
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.|
|The SAGA Geostatistics Toolbox in QGIS|
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.
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.
Lastly, grouping analysis can be examined using PostGIS, which allows for a wide variety of spatial queries using SQL, or CrimeStat.
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.