Growing use of OpenStreetMap in Government

We at ClueTrust integrated support for OpenStreetMap into both Cartographica and CartoMobile some time ago because it provides an avenue for public updating and curation that is unequaled and also provides data licensing that allows liberal reuse.

We are happy to see the wide range of OpenStreetMap in Government outlined on the OpenStreetMap.US blog.

There are lots of opportunities to improve the data set and make your mark on open data, while improving available map data for everyone.

For those with little background with the organization, it didn't start in the US, but in Europe, where most Geospatial data was (at the time) locked up behind huge licensing restrictions. To combat this, founders and an army of interested people fanned out over the continent and later the globe to create map data which was free of those restrictions and open to the public.

Cartographica 1.4.2 Available

Cartographica 1.4.2 is now available, and it's got a little bit of everything to it: new features, performance enhancements, and bug fixes.

This release ended up taking a bit longer than we expected as we have been working hard to fix the bugs reported in 1.4 and 1.4.1.    But, we haven't just been working on fixing bugs, we've also been enhancing the capabilities and performance of the existing capabilities of Cartographica, with improvements in everything from WFS compatibility to execution speed and progress information for many of our analysis tools.

I won't take up the space here to enumerate all of the changes, as they are covered almost exhaustively in the release notes, however I will point out a few of the areas we have been working on.

Analysis Tools

We introduced a lot of new tools in version 1.4, and we have been making improvements to the performance and interface for these.   In particular, almost all of the analysis tools provide more progress information now and may be cancelled.   For those who work in more than one window at a time, Cartographica no longer makes you wait in all windows while analysis is performed for one window.   This means you can continue to work on another map while performing more complex analysis on a map in the background.   All search and overlay operations are substantially improved and we are continuing to follow additional optimization paths (while maintaining accuracy).

Geocoding

Because of the Bing geocoder service that we also support, many folks don't use the built-in geocoder.  However, for tasks involving historical data, or large amounts of data being proccessed in a small area, it's often a faster mechanism (and it doesn't require a network connection).  In this release, we've significantly improved the handling of whitespace, negative address ranges in the streets files, additional abbreviations, and support for streets whose names begin with "Ste".

File Import/Export

Most of the changes to file import involved upgrading to the most recent libraries for external file formats, such as MrSID®.  These should improve compatibility and performance.

For export, we've fixed a bug involving line style export to Illustrator and enhanced the Save window to include file extensions that appropriately default for the selecte file type.

ESRI File Geodatabases can now receive the entire map instead of being broken into a set of GDB files per layer.

Live Maps

The big change for live maps is that Custom OSM layers can now point at basically any tile source that uses a similar format to OpenStreetMap but isn't actually an OpenStreetMap server.   For those of you with access to tiled services from an ESRI ArcServer map, this means that you can access those tiles from Cartographica as well. For an OpenStreetMap server, you can just use the URL of the service in the box.   For other services, you'll need to use a more complete URL with {zoom}, {x}, and {y}, representing the zoom level, x tile id, and y tile id.   Here are some examples:

For ESRI ArcServer maps, end the URL with {zoom}/{y}/{x}, so if the base URL of the map is "http://myserver/ArcGIS/rest/services/MyMapLayerName/MapServer", the URL you would use is: "http://myserver/ArcGIS/rest/services/MyMapLayerName/MapServer/tile/{zoom}/{y}/{x}".

For Google-style maps, the URL is more complicated, so if the base URL of the map is "https://mymapserver.com/foo/x=9&y=23&z=6&scale=2&s=Ga", the URL you would use is: ""https://mymapserver.com/foo/x={x}&y={y}&z={zoom}&scale=2&s=Ga".

At the moment, all of the key services are using the same CRS, which is the "Web Pseudo-Mercator" and thus Cartographica will automatically determine the right information and display it correctly.

WFS Layers

We continue our improvement of support for WFS layers.    This release includes better handling of WFS 2.0.0 servers (such as most recent versions of GeoServer) as well as backwards compatibility with older 1.1 and 1.0 services.   We also fixed a bug in handling single polygon layers in GMLv3 layers (multipolygon layers were fine).   

The Web Feature Layer Attributes window (available from Layer > Web Feature Layer Attributes... and when you add a new web feature layer using File > Add Web Feature Layer...) has been enhanced to allow limiting of requests to the server (necessary for some servers that don't know their own limitations) and the introduction of manual filters.    We hope to make filters easier in future versions, but for now if you know the XML filter language (or can have somebody work up a filter for you), then you can paste that directly into the Filter box and it will be applied to layer requests.

The Future

We have many plans going forward, including improvements to our existing features and additional features for analysis, presentation and exploration.   We sincerely appreciate your support.

Mapping Mountain Top Removal

iLoveMountains.org is an organization that, "is the product of 14 local, state, and regional organizations across Appalachia that are working together to end mountaintop removal coal mining and create a prosperous future for the region." Together this group provides information and research to the public about the problems that mountain top removal mining has cause, and continues to cause in the Appalachians. 

According to iLoveMountain.org, "Mountaintop removal is a relatively new type of coal mining that began in Appalachia in the 1970s as an extension of conventional strip mining techniques. Primarily, mountaintop removal is occurring in West Virginia, Kentucky, Virginia and Tennessee. Coal companies in Appalachia are increasingly using this method because it allows for almost complete recovery of coal seams while reducing the number of workers required to a fraction of what conventional methods require."

Part of the problem with mountain top removal is that the processes is destructive to the environment and the local habitats in the area where the mining is occurring. "Before mining can begin, all topsoil and vegetation must be removed. Because coal companies frequently are responding to short-term fluctuations in the price of coal, these trees are often not even used comercially in the rush to get the coal, but instead are burned or sometimes illegally dumped into valley fills" (iLoveMountains.org). Addittionally, "While reclamation efforts such as stabilization and revegetation are required for mountaintop removal sites, in practice, state agencies that regulate mining are generous with granting waivers to coal companies. Most sites receive little more than a spraying of exotic grass seed, but even the best reclamation provides no comfort to nearby families and communities whose drinking water supplies have been polluted and whose homes will be threatened by floods for the hundred or thousands of years it will require to re-grow a forest on the mined site" (iLoveMountains.org).

The problem that iLoveMountains.org is attempting to combat is inherently a spatial problem, and due to this fact they disseminate a lot of interesting spatial data to help promote additional research and understanding by the public. To help highlight the problem that they are pointing out this blog post uses a number of the dataset provided to create maps and help visualize the problems. To download the iLoveMountain.org data visit their data download webpage. 

Before importing any of the data, first add a Live Map by choosing File > Add Live Map. This will provide a basemap and will allow you to see what the mountain top removal sites look like based on satellite imagery. Additionally, a shapefile of U.S. States was imported by choosing File > Import Vector Data to more clearly show the state boundaries in the area. Download the states shapefile at the U.S. Census website. Once the basemaps are imported you can import each of the shapefiles provided by iLoveMountains.org by choosing File > Import Vector Data.

The first map below shows all of the mountains that are being or have been mined in the Appalachian region. This is the NRDC_500_Mountains layer. The mountains have been color coded to indicate the states that they are within. To color code the mountains double-click on the mountains layer in the layer stack, change the based on option to state, click on the gear box and select Distribute Unique Values (4), and then assign colors to each of the categories. 

The next image shows all of the active mines in the region. This is the Active_Mine_Sites layer. The large red points are the active mine locations.

The next image is a Kernel Density Map of the Active Mine sites. To create a KDM select the Active_Mine_Sites layer in the Layer Stack and then while holding down the option key choose Tools > Create Kernel Density Map. Select the Visible Area option and then click Analyze. 

The next image is a look at the satellite imagery of the largest hot spot located in Southeastern Kentucky. Notice the large gaps in the tree canopy. 

And a closer look at a large mine near Lamont, KY.

The final maps show the mines as digitized polygons highlighting the amount of area taken up by the mines. This is the Skytruth_Mines_Merge layer. 

  

Using Spatial Join to Identify Elevation for Points

An owner of a golf course is working on redeveloping his golf course and has hired an analyst to help with the process. To make the process more streamlined and efficient the analyst is using GIS to help identify and plan various aspects of his new project. As part of the project the analyst needs elevation data for the greens on the golf course. He wants to know the elevations of the greens so that he can determine the best way to add new approaches and shots. Cartographica can help in this process by using Digital Elevation Models. To obtain the elevation data the analyst went to the Kentucky Digital Elevation Model Download Center. On this website the owner downloaded the K42 DEM. 

After downloading the DEM import the file into Cartographica by choosing File > Import Raster Data. Once the DEM is imported into Cartographica you can create a contour map that will provide the needed elevation data. To create a contour map choose Tool > Create Contours. Set the increment value to 5. In the case we want highly accurate elevation data so we will use a small increment. Set the base value to 0. See below for an image of the create contour windows. 

Next, we need to create a layer showing the location of the greens on the golf course. To do this we can use satellite imagery and the Add Feature tool. Choose File > Add Live Map. Select the Bing Map layer in the Layer Stack and the choose Layer > Include in Map Extent.  Uncheck the K42 layer in the Layer Stack. Zoom in to the Lower Left Corner of the Contours layer and look for High Point Golf Club. Once you find the golf course uncheck the contours layer. Choose Layer > New Layer and then Edit > Add Feature. Select to Add a point layer and then add points on top of the greens. See below for an example of the add points process. 

See below for an image of the full set of points. 

We can highlight the general elevations of in the area by showing color differences for the various elevations. Double-click on the contours layer in the Layer Stack to bring up the Layer Styles Window. Change the based on selection to Elevation. Add four categories by clicking on the + button four times. Choose Window > Show Uber Browser and then click on the palettes tab. Select a palette and while holding down the option key, click and drag it to the table in the Layer Styles Window. See below for images of the Layer Styles Window and the Contours. 

And the Contours

Finally, we can perform a spatial join to join the elevation data from the contours layer with the greens points layer. This join will add a new column to the greens points that contains the elevation data so that the analyst can determine the elevations for each of the greens. To perform the spatial join choose Tools > Spatial Join. Select the Closest option and type in 50 meters in the distance box. The distance value is used to specify a search radius for the Closest operation, which will select the nearest feature in the Contours layer and use it as the feature to be joined. The Closest operation is preferred over the Within Distance operation in this context because the Within Distance operation will find all matches and then randomly select a value where as the Closest operation will only find the nearest feature. Uncheck the discard unmatched features box. See Below for an image of the spatial join window. 

Finally, using the Layer Styles Window you can show the elevations of each of the points using the Item Labels. See below for the final image. 

Mapping Shale Gas and Shale Fracturing (Fracking) Sites in the United States

Fracking has become a hot button topic among environmentalists and politicians over the past 10 years. Shale Gas is a type of natural gas found deep within shale rock formations. In 2000 shale gas provided only 1% of U.S. natural gas production; by 2010 it was over 20% and the U.S. government's Energy Information Administration predicts that by 2035 46% of the United States' natural gas supply will come from shale gas (Wikipedia, 2013). There is significant debate about whether fracking should be increased to these levels due to the environmental risks associated with the fracking process. Opponents of fracking argue that the extraction and use of shale gas can affect the environment through the leaking of extraction chemicals and waste into water supplies, the leaking of greenhouse gasses during extraction, and the pollution caused by the improper processing of natural gas. A challenge to preventing pollution is that shale gas extractions varies widely in this regard, even between different wells in the same project; the processes that reduce pollution sufficiently in one extraction may not be enough in another (Wikipedia, 2013). However, proponents of fracking argue that shale gas represents a significant improvement when compared to other fossil fuels, and may even help stem growth in greenhouse gases. Also, shale gas and the associated industry can greatly improve the United States' ability to become energy efficient and self sustaining. No matter you perspective on this issue it may be of interest to you to find out where fracking and the related industries are taking hold in the U.S. In this post we use data from U.S. Energy Information Administration. The image below shows the basic idea behind fracking. 

The first map below shows the shale basins within the U.S. These are areas where there is enough shale gas fracking to occur. To download this data click on the EIA link above and then scroll down about half way down the page until you see the "Geospatial Data in Shapefile (.shp) Format". Click on the the Shapefiles for Basin Boundaries under the Data for Shale Plays Map heading. To import the data choose File > Import Vector Data. To add the live map as a basemap choose File > Add Live Map. 

 The second map below show the areas known as Shale Plays, which are areas that have shale gas currently being harvested using fracking. The areas being harvested are dark red.

 

Recently, fracking made the news when NASA released satellite imagery highlighting the growth in fracking in undeveloped areas around the country. Specifically, NASA highlighted the growth in nighttime lights in the North Dakota region, which is a relatively undeveloped part of the county. Despite the low levels of development there are significant nighttime lights visible in areas where shale gas fracking operations are underway. Download a Geo.tiff image created by the Visible Infrared Imaging Radiometer Suite (VIRUS). Once you download the image you can import it into Cartographica by choosing File > Import Raster Data. Below is an image showing the geo.tiff with the Shale Plays layer. 

And a closer look at the increased nighttime lights due to fracking operations in North Dakota.