Global Climate Monitor: "Getting Knowledge from Data"

The Global Climate Monitor (GCM) was created by researchers at the University of Seville to "model and [g]eo-visualize global climate data and climate-environmental indicators." If you are familiar with NOAA's monthly climate reports, you will find many of the same types of information here.   Finding and accessing weather and climate data can be daunting.  So, any website that tackles the challenge of making spatiotemporal data more accessible gets kudos.

Users can query several different datasets.

Users can query the GCM database by monthly and annual values as well as normals and trend back to 1901.  Here values are displayed in a grid (with each cell representing roughly 34 x 34 miles)

Moreover, the website allows users to easily select and interact with data across the globe.  In addition, a data download tool has recently released.  It allows users to download the onscreen data as a square grid in many different commonly used formats (*.kml, tiff, jpeg, csv, xlsx, and shp).

In order to download data, look for the little gift/package button on the right-hand side toolbar--at the bottom.

The three goals of the project are: 1) Analysis and management of climate information, 2) spatio-temporal climate variability, and 3) Applied Climatology.

Lastly, the project uses open source web map development tools including GeoServer and OpenLayers.

For more information, visit the main GCM website at http://www.globalclimatemonitor.org/ or the project page at http://grupo.us.es/climatemonitor/.

Above: Monthly temperature anomalies for February 2015.  The website allows users
 to choose a basemap, change transparency of layers, and identify values. 

The project leverages popular free and open source web map development tools.

GWR4: Software for Geographically Weighted Regression

Today, I will write about a free program that I think remains unknown to many people.  If you are interested in regression models, you will want to keep reading!

GWR4 was developed by the same scholars that created Geographically Weighted Regression (GWR) (Brunsdon, Fortheringham, and Charlton).  In brief, GWR runs local regression models on each geographic feature vs. ordinary least squares (OLS) regression which globally runs one model on all features.  In GWR, coefficient estimates are allowed to vary geographically and can be mapped.

• Questions that GWR can help answer:
• General: Do the effects of demographics race, income, education vary geographically on your outcome of interest, after statistical adjustment?

• Specific:  Does the percent of smokers on prevalence of asthma vary geographically or stay the same? 
• Since smoking is a major risk factor for asthma, we would expect all local coefficients to be positive--increasing the prevalence or risk of asthma.

GWR4 helps calibrate and run GWR Models. Moreover, it allows for models besides the normal/Gaussian distribution--such as Poisson (for counts and rates) and Logistic (odds).  For people interested in counts and rates, it is vital to be able to use the Poisson distribution.  Currently ArcGIS only uses OLS models.  GWR is a free program but is copyrighted.  It runs on Microsoft Windows.

"GWR 4 can be used to explore geographically varying relationships between dependent/response variables and independent/explanatory variables."

If you are still confused, click the map of Tokyo below--based on GWR4 and the sample data.  Hopefully, this will help clarify the significance of GWR models. (I ran out of time or I would have classified the layer better.  Keep in mind, odds  > 1 each unit increase in the unemployment rate is associated with higher mortality, odds < 1 each unit increase in unemployment rate is associated with lower mortality.)

Odds of the effects of the unemployment rate on mortality among the working age population.  Each of the 262 municipalities in Tokyo has an estimate of unemployment's contribution--adjusted for other variables in the model.

GWR4 has an intuitive user interface, good manual, and sample data with its associated publication. Your sessions/selections  of data, model, specifications, etc. can be saved by going to the file menu.  Be sure to check it out!

Visit here to download GWR4: http://www.st-andrews.ac.uk/geoinformatics/gwr/gwr-software/

GWR's Interface and Workflow: Load data->Identify Dependent and Independent Variables-->
--> Choose kernel, bandwidth, and selection criteria-->Output and Execute!

A quick note: I am taking a break from OpenLayers 3 (OL3) to wait for the final code, documentation, tutorials, and books to be completed.  However, we will return to OL3 later this year--when I also hope to have more examples.  Be sure to check out other JavaScript libraries to get maps on the web from Leaflet,  MapBox,  Google, CartoDB, and any others I missed!  For more information on other similar projects visit the GIS StackExchange.