An Introduction to OpenLayers 3, Part I

OpenLayers is a JavaScript library for creating interactive maps on the web. Basically, these libraries are referenced to help facilitate the development process, providing a basic foundation and advanced features.  I will devote several posts to OpenLayers 3 (OL3) which is currently in its beta release.

This first post will demonstrate some of the basic code and functionality of OpenLayers, and why you should consider using it.  Later posts will cover topics that build logically on one another and include moving and copying files to a web host, symbology, and allowing for user interaction (and feedback) with a map.  Lastly, we'll look at incorporating other free and open source GIS software, such as GeoServer.

OL3 will improve on its predecessors with a more streamlined code, better templates, and documentation. For a brief history of OpenLayers, head over to Wikipedia.  OpenLayers can handle a wide range of map-related files and extensions.  It is client side (I will talk more about this in later posts).

OL3 will improve on OL2.

Loading a Basemap
A logical starting point is loading a basemap, which many maps and map apps utilize.  One of the earliest decisions you may face:  ow much of the user's screen a map should take up.  For the web version, visit: http://webmapexamples.net/OSMBasemap.html.  Feel free to zoom and pan.  Other open basemaps, for example Stamen maps, can also be loaded. A screenshot appears below:

As seen in Mozilla's Firefox web browser

You can download the code with comments in a text file here or html file, which you can experiment with after downloading to your desktop.

A different sized map is at: http://webmapexamples.net/OSMBasemapv2.html.  A simple change to the height of the style was made and is highlighted in red.

  <style>
      .map {
        height: 350px;
        width: 100%;
        }
    </style>

Initially, OL3's learning curve remains fairly steep.  However, coding appears to be much more efficient than OL2 and gets easier over time.  Advanced functionality can be easily added, a definite bonus!  An early OpenLayers 3 workshop is available here (note: try reloading the page).  Tutorials, examples, and documentation are still being produced.  Looking ahead, at least two books will be published to assist you:

• OpenLayers 3: A Beginner's Guide
• The Book of OpenLayers 3

Next time, I will write about moving and copying OL3's libraries. to a web hosting service and getting them on the web! I will also talk about key additional steps and importing common layer files such as KML files.

Exploring Health Insurance Estimates by County Using GeoDA

Health insurance has been an important topic over the last several months, with the opening and closing of open enrollment at HealthCare.gov.  Most recently, the Census released its first estimates of health insurance coverage at the census tract level.  Typically, estimates have been made for county-level data which is what I explore here from the Small Area Health Insurance Estimates (SAHIE) Program.

I used the latest build of GeoDA 1.5/Beta/preview to explore spatial patterns in 2012 estimates for the percent of population that is uninsured under age 65 by county.  I examined a univariate Local Moran's I and a bivariate example using the percent below the poverty level.

If you have not used GeoDA before to conduct exploratory spatial data analysis (ESDA), you need to give it a try.  The latest build features more data import/export and editing options, a significant improvement over earlier versions.

Some of GeoDA's features are either a) not present in ArcGIS and its extensions or b) only found in ArcGIS Advanced, formerly ArcInfo, namely the creation of spatial weights using polygon contiguity/adjacency. (Note: You can create weights in ArcGIS, based on distance for example.)

To help keep all maps uniform, I imported the results into QGIS. Click on any image below to magnify it. You can find definitions for the terms and statistics used here.

Map of Percent Uninsured by County
Regionally, the South and West US have a smaller percent of counties with low rates of uninsured compared to the Midwest and Northeast. Or rather, they have a higher percent of counties with high rates of uninsured.

For the official map, for comparison, visit here.

LISA Map of Percent Uninsured by County
The map below shows clusters of counties with high, low, low-high, and high-low rates of uninsured.  Light grey areas were not statistically significant.  Spatial weights were created for queen contiguity, 1st order/neighbors.

Global (p=0.02) and local autocorrelation are present.  

The Moran scatter plot of percent uninsured vs.
lagged/neighboring counties has a r-squared value of 0.74 

LISA Map of Percent Uninsured and Percent Below the Poverty Line
Lastly, I examined a bivariate LISA of the percent uninsured and percent below the poverty line (all ages). For this map, I also included the outline of states.  Interestingly, there was no across-the-board global association, as one might expect.  However, state policies undoubtedly affect the percent insured.

No global autocorrelation (p=0.51) but local autocorrelation is present in parts of states or throughout most of particular states, for example the low percent uninsured (and low percent in poverty) in Massachusetts which underwent significant healthcare reform in 2006.  What do you think about some of the other states?

Affordable Care Act Implementation
Unfortunately, some of the states that could benefit the most from the Affordable Care Act (ACA) did not move to implement, as evidenced in this map from the Commonwealth Fund.

Those States sprinting ahead with implementation and those sitting it out.

Bottom line: GeoDA and QGIS are a potent combination.  GeoDA's import, export, and data editing features are much improved.   It is a vital tool for learning and conducting spatial analysis.  However, a few other components of GeoDA are worth mentioning including: making cartograms and conditional maps, connectivity histograms, and performing spatial regression.  As implementation of the ACA moves ahead, it will be interesting to see changes or lack of changes in the percent insured.

QGIS Tip:  Save the symbol styles (categorized) for the cluster types (LISA_CL variable) after you make them, since they can be saved, loaded, and used again for any map layer created in GeoDA as long as you don't change the default variable names.  This is a huge time saver. 

More 3D Maps and Tips Using QGIS2threejs

After last week's popular post, here are few more maps and tips using the QGIS2threejs plugin.

The Maps

1. 3D Chloropleth Map of Crime Counts by Police Beats in Chicago, 2013
2. Visualization of Mt. Rainier, Seattle
3. Groundwater Depths in Three Counties in Nevada

Data sources and references:
Open Data Chicago
Chicago: Official Police Maps (CLEARMAP)
National Map Viewer
Nevada Bureau of Mines and Geology

When viewing the maps, if a black box appears instead of a basemap, try refreshing/reloading your browser and the basemap should display properly.

The Tips

3D Chloropleth Map:  Here is a trick to create a 3D chloropleth map on a flat surface.  The plugin won't run without a digital elevation model (or DEM) raster, but you can 'trick' it into using a flat raster.

• Updated(12/9/2015): The plugin now allows users to select a flat surface/no DEM used
• Take the raster calculator and exponentiate/raise all cell values to the power of 0 (rastervariable^0). The resulting raster will be flat with a value of 1.  Alternatively, multiply by zero.  (You may also have to symbolize null values as well.)
• Classify and symbolize the polygons using your desired color scheme--before using the plugin
• In addition, I changed the color of the raster file in QGIS to match the background in the *.html file (#ccccff). 
• I added HTML code for a basic legend using frames (90% for the map, 10% for the legend)

Basic HTML code sourcing the two frames.  The "Chicago" frame was generated from QGIS.
The "Legend" frame was a simple piece of code directed to a JPG of the Legend.

Screenshot of the final map and legend.

Mt Rainier:  Instead of using a web service, I download imagery and used the Build Virtual Raster in QGIS.   I used a detailed DEM and imagery from the USGS's National Map Viewer.  You can download files from USGS by using a bounding box, for example.  I used a vertical exaggeration of 1.1 in the plugin.  Click the screenshot below to enlarge a comparison of between Google Earth and the plugin's version of Mt. Rainier.

Left: Google Earth, Right: Plugin result using files from USGS

3D Groundwater Depths / Underground:  Using point data with depths, you can either select a negative multiplier in the plugin or create a new variable and multiply your depths by -1.

If your depths are positive values, select a negative multiplier in the plugin.

An overhead and underground view of the map.
The orange boundary represents three counties of interest.