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.

Crime Analytics for Space-Time (CAST)

Crime Analytics for Space-Time (CAST) - alpha (2013) is a free and open-source cross-platform program (Windows, Mac OSX and Linux) designed "to detect spatial patterns and trends in crime data."

CAST is a nifty piece of software that combines key functions of other programs from the ASU GeoDA Center with a greater emphasis on temporal trends.  It even allows you to view data by calendar days, months, and years.

What you will need...
All you need is a shapefile of projected crime incident data and any boundary files (posts, census tracts, blocks, etc.).  To perform some types of cluster analysis, you will also need to aggregate your data.  If all you have is a *.csv, you can use QGIS to crate a shapefile file and save it for importing into CAST. There is sample data from San Francisco available here.

Check the format of your date field
If you are having trouble with using your data's date fields in CAST, use QGIS's field calculator to create a new variable with an output field type of "Date." Next, use "Conversions" and "to date" under the function list, and make sure the variable you enter is in the format "YYYY-MM-DD".  So, the expression should read:  todate( "Date Field" )

• Make sure to create separate fields for date and time--rather than one single field. 
• I tried a couple different formats but this format worked. 
• In CAST, you will be able to select your date field. 

CAST can do a lot!
In the "Tools" menu, you can create a grid and save it as a shapefile.  Under "Table", you can view attribute data for your shapefile. The "Weights" menu allows you to create spatial weights and view a connectivity histogram, like in GeoDA.  The "Map" menu allows you to symbolize polygons by several different criteria.  The real fun comes in the last four menus: Calendar Map, Cluster Map, Time, and Explore.

Calendar Map + Dynamic Map
One of CAST's interesting features is combing a calendar with the number of events and a map.  You can add shapefiles and layer them, although a bit tricky by clicking on each. Below are few examples using homicide data from Open Data Philly.

A calendar and map of homicides in Philadelphia starting in 2006.

Clicking a calendar will bring up a bar and pie graph (which can show a
breakdown if there is more than one category of crime/event in your data).

Graphs and maps are linked in CAST, so clicking on a feature or peak in a graph will highlight the selected features.

Like other ASU software, all graphs and maps are linked. Here one line was selected,
which represents one neighborhood, and the corresponding area on the map is highlighted.

Cluster Map
You will find a lot of tasks that are identical to GeoDA here plus some dynamic density maps.

Time & Explore
Trend graphs area available here as well as standard graphs like a histogram, scatterplot, and boxplots.

In sum...
Looking at patterns over space and time is difficult, but CAST can help.  Unfortunately, you cannot save a session in CAST so be sure to keep track of what steps you perform.  Limit your data to the time period of interest. I would not recommend adding huge point shapefiles.  Lastly, I was not able to save a movie/*.gif of animations over time, but if I figure it out, I will update this post.

For more information:
CAST Manual