March 31, 2012

Conditionals in the QGIS raster calculator

I needed to do some conditionals in the QGIS raster calculator, but it doesn’t support that—or at least doesn’t seem to. But it does support logical operators, with a result of either 0 or 1. For instance, here’s the script I wrote:

# Subtract them
((DavisQuad2012-02-25T16_00_00Z@1  -  DavisQuad2012-02-29T16_00_00Z@1)*
# Multiply by 1 if neither is 255 (NoData), 0 otherwise
(DavisQuad2012-02-25T16_00_00Z@1 != 255 AND DavisQuad2012-02-29T16_00_00Z@1 != 255))
# Subtract 32768 if either one was NoData, giving us -32768 for NoData.
-
(32768*(DavisQuad2012-02-25T16_00_00Z@1 = 255 OR DavisQuad2012-02-29T16_00_00Z@1 = 255))

Of course, you can’t actually put the comments in. But what it does is this: First, I subtract one raster from the other and multiply that by the logical operation that neither one contains NoData. That gives me the difference of the rasters, or 0 if either one contains NoData. Then I subtract 32768 multiplied by the inverse of the aforementioned logical operation, so any pixel with a NoData value in either of the original rasters is -32768 in the new one.

March 3, 2012

Conditional Labels in QGIS

I fairly commonly find myself in a situation where I would like to display one label for certain features and another for other features in the same layer. QGIS doesn’t have an official way to split labels up into categories, and until now I’d resorted to having two layers to render otherwise identical features. But, in the new, excellent expression based labeling from Nathan Woodrow, I realized one can use an SQL CASE statement. For instance, one time I need to use two types labels is when labeling roads in OpenStreetMap: I want to use the name tag, unless the feature has a ref tag defined (a name tag might be ‘Capital City Freeway’, while the ref tag would be ‘US 50’). Here’s how I solved that particular problem:

February 2, 2012

More Basemaps in QGIS

One of the more popular posts on this blog has been my piece on adding basemaps to QGIS. While the OpenLayer plugin is great, one of the things that I find dissatisfying is that it requires reprojecting your data to match the EPSG:3857 basemap. I often work in State Plane, and I’d just as soon have my data stay in that projection, which will also minimize local distortion. Well, as it turns out, one can add tiled map services as GDAL raster layers, with all the benefits that entails (e.g. reprojection). What you need to do is create an XML file like the following (which is lifted almost verbatim from the GDAL website, specifically this file):

December 30, 2011

Transit to Everywhere

Data courtesy MapQuest and OpenStreetMap CC-BY-SA, the City and County of San Francisco, and Bay Area Rapid Transit

This is an overlay of the transit and walking trip plans generated by OpenTripPlanner from Powell and Market to every other intersection in San Francisco, after Eric Fischer’s map of walking routes to every intersection in San Francisco. It brings out the transit routes but also shows well-used walking routes. The lines do not vary in width (don’t let Market Street fool you, it’s actually several lines—BART, MUNI rail in 2 directions, Muni bus, walking—very near each other). The lines fade where there are fewer routes using them, because they are rendered as black set at 10% opacity. Where there are more lines overlapping, the lines become darker, in what I believe is a log (or log-like) scale. It ended up just mostly being a map of San Francisco, with transit routes emphasized. It doesn’t show potential utilization of the transit system, because the routes are not weighted (it would probably be wise to weight the routes by the density of the block they terminate in and by their service area; i.e., estimate the number of people within the Thiessen polygon of each intersection and weight the route by that). Also, I had difficulty finding an opacity level where the usage of transit routes fades towards the end (as it clearly should) but still shows the streets that walked down by just one or two trip plans.

December 23, 2011

GitHub Image Diffs

As you may have gathered, I like Git and GitHub. Today, I ran across a GitHub feature that is really cool and above and beyond the call of duty: not only do they produce and display diffs on text files, but also on image files! You can see an example in one of my repositories. Added points if you can figure out where the map tile is from!

December 13, 2011

Shapefiles in OpenLayers

Update 2011-12-14: It seems that a lot of people are coming here from web searches with phrases like “shapefile openlayers.” If all you want to do is display your data in OpenLayers, I’d highly recommend using a program like Quantum GIS to convert your Shapefile to a more web-friendly format like KML or GeoJSON. Both of these formats can be read by OpenLayers directly, and you’ll see faster performance and more browser compatibility than if you were to load your Shapefiles directly.

November 12, 2011

Another LA Metro Visualization

Here’s another visualization of the data used in the previous post; I made the lines a lot finer, so the noise is less visible. It’s easier than ever to see the Silver Line. I classed the data manually this time.

November 12, 2011

Making Transit Travel Speed Maps with Open Source GIS

Update 2011-11-12 8:21 -0800: I just posted a visualization I like better.

The Internet has been abuzz the past week regarding transit speed maps. It seems to have been spurred by a post on Bostongraphy, which was inspired by many of the amazing visualizations produced by Eric Fischer, especially this one. Indeed, this blog has gotten a fair bit of traffic itself, because Andy Woodruff of Bostonography used my avl2postgis project to retrieve the data.

October 29, 2011

Google Maps Tile Scales

I found this buried deep in an appendix of the Mapnik XML Schema Reference, and I thought it so useful I am reposting it here:

Zoom level Scale denominator

559,082,264

279,541,132

139,770,566

69,885,283

34,942,642

17,471,321

8,735,660

4,367,830

2,183,915

1,091,958

545,979

272,989

136,495

68,247

34,124

17,062

8,531

4,265

2,133

1,066

533

These are used not only by Google Maps, but also by Bing Maps, OSM, CloudMade and many others, and in fact just about any Google Mercator tile source.

September 8, 2011

Mapping Real-Time Delays: Review

Some readers may have noticed that I’ve updated my last post several times in the last few days. After thinking about the algorithms I used, I realized there were some significant issues with them. I’ve explained them a certain amount in my updates to my previous post, but I’d like to expand on the issues a bit here.

Using an Inverse Distance Weighting algorithm exaggerates delays where stops are sparse by allowing them to spread over larger areas; the graphic should make this clear; if the red dots are stops with delays, one in the city center and one in a suburb, it is clear that the delays will be magnified where stops are sparse (figure 1), because there are less stops around it.