Cartographica version 1.4.7 shipped on July 1, 2016 providing a few bug fixes and a crucial change to ensure compatibility with updates to the Bing Maps API that we use for Live Maps.
We encourage everyone using macOS 10.7 or more recent to upgrade to Cartographica 1.4.7.
Release Notes for 1.4.7 are available from MacGIS.com.
In June, Apple announced pre-release availaibility of iOS 11 and macOS High Sierra (10.13). ClueTrust is working to verify complete compatibility with these versions prior to their release this fall.
However, as a rule, we caution against the use of pre-release operating systems for crucial work. Although we are unaware of any problems today that hamper the use of either Cartographica or CartoMobile, it is always possible to experience crashing and data loss with operating systems that have not been fully tested.
With that said, if you choose to experiment with iOS 11 or macOS High Seirra and run into problems with our software, please don't hesitate to report the problem to us. We can't guarantee we will fix it during the beta period, but will work hard to have any compatibility issues resolved before the final release of the operating systems.
After quite a long time in beta and development, ClueTrust is pleased to announce the release of CartoMobile 1.4.4, our iOS App for field data collection and use.
There are a lot of changes in 1.4.4, probably quite a few more than would otherwise be indicated by the small bump in version numbers. However, we have put out over 30 test versions since the last release in order to make sure this version is ready to go.
The last version, 1.4.3, was remarkably crash-free. We expect this version to be similarly reliable, although not similarly long-lived.
Version 1.4.4 will be the first version leaving behind all versions of iOS prior to 8.1. We take deprecation of operating system version compatibility very seriously, but our statistics indicate that almost all of our active CartoMobile users are running iOS 8.1 or higher (mostly iOS 9+). We expect that the next major release will remove support for versions prior to iOS 9, as the move forward in iOS operating systems continue.
Changes
There were a lot of individual changes to CartoMobile 1.4.4, but here are the main ones:
Please feel free to contact us via our support site if you have further questions on any of these changes or if you need assistance.
Also, please use our Feedback button in the Gear menu to provide us with suggestions or complaints.
Lastly, please rate us on the App Store. Each time we release a new version, the stars for that release disappear, so we'd like to get our ratings back up!
Thank you,
ClueTrust
If you have version 1.4.6 (the current shipping version since May 2015) or greater, then your Cartographica is not susceptible to this problem. If you are running 1.4.5 or lower, please read this message.
The updater that we use in Cartographica (Sparkle) has been found to have a specific set of vulnerabilities that can cause remote execution.
The specific problems can be exploited via a MITM (Man-in-the-Middle) attack, resulting in arbitrary code execution in the Javascript portion of the display that Cartographica shows during updates.
Unbeknownst to us, we mitigated this problem last spring when we moved to using SSL/TLS for our updater feed. As such, if you've kept your copy of Cartographica up to date, you're in good shape.
The next release of Cartographica will include a further update to the underlying software updater which takes more steps against this a similar vulnerability occuring in the future.
If you are running a version less than version 1.4.6, we encourage you to update directly by using the Cartographica Download Page directly. Once you've done that, the updater should be sufficiently secure.
Cartographica version 1.4.5 shipped on March 2, 2015 providing mostly bug fixes for the 1.4 series and increasing compatibiility with large-record shapefiles (contrary to the documentation, a number of products, including ESRI's products, are now using a 32,767-byte limit instead of a 4,095-byte limit for records in the DBF files.
Release Notes for 1.4.5 are available from MacGIS.com.
Cartographica has for a long time supported geocoding photos based either on tracks or based on data inside of the photos themselves. For most of that time, we have supported doing this for bothfiles on disk and images that are stored inside of Apple's iPhoto application.
With recent versions of OS X (basically since 10.8.5 or so), Apple has been providing access to photos in both iPhoto and Aperture through the file selection or Open dialog as well by way of the Media item in the sidebar on the left.
Since the introduction of that feature, we have supported both that mechanism, and our older direct-iPhoto access method (which had been supported since 10.6). However, recent changes to iPhoto and the fact that this mechanism actually has better previewing than our old system has led us to the decision to remove the iPhoto-specific menu item for Geocoding photos.
As of Cartographica 1.4.5, we are going to remove these menu items. We are not removing the ability to geocode your iPhoto photos, you will just need to access them through this mechanism.
We will update the documentation and the support site to reflect this change.
There's a great article from VerySpatial making the case that the use of "analog" maps in cases where differential information is being compared might night be the best way to display data online. We've been looking into this space recently and already have excellent KML Export in Cartographica, which can be easily paired with online tools like Google Layers and Leaflet.
The original article from The Washington Post is an interesting piece about use of time across the US.
We are pleased to announce the release of Cartographica version 1.4.3, our latest minor feature and bug fix release. As with previous versions of 1.4.x, 1.4.3 is compatible with OS X versions 10.7, 10.8 and 10.9 (and we've also done some preliminary testing on 10.10).
Features that we have included in the 1.4.3 release include:
These accompany an array of bug fixes described in more detail in the Release Notes for 1.4.3.
This update is avialable to all customers. In addition, we are resetting the clock for trials, so if you have tried 1.4.2 or earlier and want to try again, please visit the Lost Key page and enter your registered email address to receive a 10-day trial of 1.4.3.
One significant addition to 1.4.3 is that of the Selection Summary window, which shows statistical information for the currently selected features. In the past, Cartographica has provied the Selection Info window, which allows you to peek at the details of the selected items, and this new option expands on that to provide statisical information about the selected items. In particular, the Count, Sum, Average, Minimum, and Maximum values of the user-selected column are shown in the Selection Summary window, so you can quickly identify this information about any selection and any column.
In addition, when the selected column is a Calculated Column, Cartographica allows you to choose Formula calculated using summary values which calculates the formula over the summary values as opposed to creating the summary values from the post-formula values. Although in the case of simple formulas, this may have similar results, for some formulas it provides a different insight. It is important to understand these implications, as the default case (sum of the formula values, average of the formula values, minimum of the formula values, minimum of the formula values) can differ greatly from the application of a formula to the sums of the components, the averages of the components, the maximums of the components, and the minimums of the components. In most cases, the default case is appropriate.
Additionally, we have moved to linking against the 10.9 libraries in OS X. This is an internal change and should have no noticable effect on customers who are currently running Cartographica under 10.8 and 10.7, as we have taken pains to isolate the use of the few 10.9 dependencies and made sure that they are only used under 10.9. Further, our automated test systems are run on 10.7, 10.8, and 10.9 to ensure maximum compatibility. However, there were some changes to the way internals are handled under 10.9, which required us to spend more time on this update than originally expected.
The USGS has announced that the National Atlas will be taken out of service as of September 30, 2014. According to the detailed product availability information, much of the data will be available via the to-be-enhanced National Map system, or as data downloads "at no cost from Earth Explorer".
This transition appears to be mostly a resource realignment (basically reducing the number of sources and web sites they need to maintain.
In addition, the USGS has also announced that "[L]ater this year we will deliver these new framework datasets at one million-scale: networked hydrography, updated streams and waterbodies, streamflow gaging stations, cities and towns, Federal lands, Indian lands, wilderness areas, and urban areas."
For those of you who are using USGS WFS and WMS services, based on the National Atlas, be warned that the services will be taken away September 30, 2014, but that the USGS is " currently evaluating options for continuing small-scale Web services beyond September 30, 2014, and will provide updates here."
Scientific American posted an overview about tectonic plate movement, datums and geodesy. The article covers a fair amount of ground in some depth, but not so much so that it's confusing. Check it out and contemplate the amount of work that we'll all be doing when the USGS updates the datum in another few years.
For those who are curious, Cartographica maintains a large list of datum and datum shift information so that it can accurately overlay coordinates from one system onto another. These calculations aren't trivial and we had quite a time squeezing these conversions into the first version of CartoMobile within the space and processor constraints. However, it all paid off as the iOS ecosystem has gotten stronger and we've been able to maintain accuracy between the iOS and OS X versions.
We have been testing against Mavericks since the early betas and the 1.4.2 version of Cartographica works well with it. At this point, we aren't taking advantage of any Mavericks-specific features, but we have found on our test machines that the upgrade is a good performance move, especially for laptops.
Other ClueTrust software, such as LoadMyTracks, has also been tested and works fine with Mavericks (excepting, of course, the long-standing issue with the Mac AppStore version of LoadMyTracks and serial devices due to Apple's policy change).
Although we haven't tested any of the 1.2.x versions on Mavericks in detail, we have done basic compatibility tests with that version and it appears to also be working fine. If you find any issues, please alert us to the problems, but we would strongly suggest moving to 1.4.2 if you are running Mavericks.
For the future, we're contemplating what the move to free for Mavericks means for Cartographica's OS support. There are some clear benefits to moving to 10.9+ only for future versions and we are contemplating this right now. Considering that we are already 10.7+ only and there were few machines that were abandoned by 10.9 which could also run 10.7, it is likely that we will make this leap. If you have a strong feeling about this issue, please contact us.
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 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.
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).
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".
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.
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=GaAt 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.
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.
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.
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.
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.
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.
Live Maps can be used for many purposes. On this blog we have highlighted the use of Live Maps for georeferencing images, identifying geological features, and providing context to local area studies. Another useful purpose of Live Maps is locating objects that are tied to specific places. What does that mean? It means we can look at places where we expect things to occur and make observations at those locations. In addition to making observations and identifying objects we can also use Cartographica to analyze what we see.
If you have followed this blog at all you might have notice that I have a light obsession with all things military. Part of the reason is that military objects (especially naval) make interesting maps. Over time I have determined the locations of several interesting military ships and before Bing Maps gets updated I wanted to provide a look at these locations while also conducting some analysis and highlight a few of the functions of Cartographica. Below are a few descriptions of some of the most recent additions to the United States and Chinese Navies.
The U.S.S. Gerald R. Ford (CVN 78):
The U.S.S. Gerald R. Ford is the newest addition of the U.S. Navy Aircraft Carrier and is the first ship in the new Gerald R. Ford Class of supercarrier. The new carrier comes at a cost of $13.5 billion and it includes numerous improvement over past classes of carriers. One of the biggest improvements is the aircraft launch system which moves from steam power to electromagnets. Like other nuclear powered carriers the GRF will have an unlimited service range for a period of 25-30 years and will only need to come to port for supplies and regular maintenance. The ship is being built by Huntington Ingalls Industries in Newport News, VA. Below is an image of the port where the ship is being constructed. Like a game of ISpy, do you see the ship?
To highlight the location of the of the GRF add a new feature by choosing Layer > New Layer and then Edit > Add Feature. Select to add a polygon feature and then trace the outline of the ship.
A closer look at the U.S.S. Gerald R. Ford under construction.
Chinese Aircraft Carrier: Liaoning
Recently the Chinese military acquired an 67,500 ton Soviet era aircraft carrier and has spent the past several years refurbishing and upgrading the ship to make it battle ready. Last November, China landed its first plane on the surface of the Liaoning. See this CNN video of the plane landing. Below is an image of Dailan, China where the Liaoning has been under construction for several years (its has completed tests in the Yellow Sea). Again, like a game of ISpy, do you see the ship?
We can again create a new feature to show the location of the ship choose Layer > New Layer and then Edit > Add Feature. Select to add a new polygon feature and then draw the outline of the ship.
A closer look:
Based on the polygons that we have create we can use Cartographica's table tools to help enhance what we know about the ships. First, add a new area column to each of the new polygons so that we can see the size of each ship's deck space. To add the area column choose Tools > Add Area Column. Based on this analysis the Gerald R. Ford has a deck space of 32,356 square meters and the Lioaning has a deck space of 27,494 square meters. Also, we can add polygon coordinate columns which will give us the coordinates for each of the ships. To add coordinate columns choose Tools > Add Centroid Coordinate Columns. The final map below shows the general location of the ships.
A new feature in Cartographica version 1.4 is the ability to create contour maps. In GIS, a contour line joins areas of equal elevation above a given level, such as mean sea level. A contour map is a map illustrated with contour lines, which show valleys and hills, and the steepness of slopes. In this sense a contour map can be very useful in many contexts. One method for creating contour lines is by using Digital Elevation Models (DEM). A DEM is a digital model or 3D representation of a terrain's surface — commonly for a planet (including Earth), moon, Mars, or asteroid. Typically DEMs are created based on data that are retrieved through remote sensing technology. Remote sensing technology typically refers to and includes specialized sensors that are attached to various satellites or aerial vehicles that makes observations on the surfaces of their target object.
In this post we emphasize the use of the new contour mapping functions in version 1.4 by using data from the Kentucky Division of Geographic Information. The data on the DGI website are free for download and include DEMs for the entire state of Kentucky. To illustrate the contour mapping we will use data from the Middlesboro, KY area. An interesting geological feature about Middlesboro is that experts believe that its location between Pine Mountain and the Cumberland Mountains is actually an ancient crater from an asteroid impact. This fact makes Middelsboro among the few cities in the world that is seated within an impact crater.
In order to view the entire area for Middlesboro we actually need to download 4 separate DEMs. We need the cells U47, U48, V47, and v48 from the DGI link listed above. To download the DEMs individually control-click within each cell and then select Download Linked File as. Save the Files to your Desktop.
To import the DEMs choose File > Import Raster Data. The following image shows what your map should look like.
Notice the blue colored circular area near the center of the map. That is the location of Middlesboro and the fairly clear outline of an impact crater. Next, we are going to create contour lines for each of the DEMs that will show the differences in elevations throughout the map. To create contour lines select the DEM layer in the layer stack and then choose Tools > Create Contour Lines. This step has to be done individually for each of the DEM layers. A window will appear that allows you to choose the increments and the base. Select 50 as the increment and leave the base at 0. See below for the contour window.
When the contour lines are created they will all be black in color. In order to enhance the visibility of the contours we need to adjust the color schemes. Double-click on the U47 DEM in the Layer Stack. Change the Based on selection to Elevation. Click the + button below the table 6 times and then click the Gear box and select Distribute with Natural Breaks (Jenks). Next, choose Window > Show Uber Browser, click on the Palettes tab, and while holding down the option key click and drag a color palette of your choice into the table within the Layer Styles Window. In the map presented below a blue-red scheme was used where blue = lower elevations and red = higher elevations. Repeat this process for each of the DEMs. See the images below for an example of the Layer Styles window and the map.
Layer Styles Window:
Contour Line Map:
The next image is a closer look at the Impact Basin. Also the following image has the DEM layers turned off and a Live Map added. To add a live map choose File > Add Live Map.
Recently, authors David Weisburd, Elizabeth Groff, and Sue-Ming Yang published a book titled The Criminology of Place: Street Segments and Our Understanding of the Crime Problem. The crux of the book is that in Criminology and other disciples have a long history of using geographic units of analysis that are area based. That is, we have become accustomed to using polygons as units of analysis. Part of the reason for the use of area based units of analysis is that demographic and social data are available through sources like the United States Census Bureau. In the book, the authors make a compelling argument that area based studies miss a lot of spatial variation that occurs at lower levels of analysis. In an attempt to combat the loss of information through aggregation the authors show that using street segments as units of analysis may be a viable alternative to area based units of analysis.
In the spirit of using street segments as units of analysis we thought highlighting how to use Cartographica to aggregate data points to street segments would be a fun and interesting exercise. Recent upgrades to Cartographica 1.4 include the ability to spatially join data based on their spatial association. This includes joining points to lines.
The data being used in this example are from Washington D.C.'s GIS repository DCGIS. The data are 2011 crime incident locations and a street file containing all of the street segments in the city. To import the data into Cartographica choose File > Import Vector Data. Below is an image of the data described above. At first glance it appears as though the points are "on top of" the line segments. Observe.
However, upon closer inspection you can see that the points are not directly on top of the lines. As a result, we will need to use a distance based spatial join to join the crime points to the line segments.
Because we want to join the crime points to the street segments we first need to select the Streets layer in the layer stack. Once selected, choose Tools > Spatial Join. The following image shows the set up for the Spatial Join. Uncheck the Discard Unmatched Features so that the new join layer will have all of the street segments including those that have zero crimes. Note that the "Within Distance" operation is selected and a distance of 50 meters is the designated distance. Also note in the table that the 2011 crime data fields are set to ignore. The reason for this is that at this point we do not need all of the excess crime information. All we want is the counts of crimes on the various street segments. Finally, note that the last field in the table "Join Count" is selected to be copied. This will be the new field on the street segments layer that will contain the crime counts.
After the points are counted and aggregated to the street segments you can reclassify the counts into groups and then vary the street segment colors by the groups. Double-click on the streets join layer in the Layer Stack to brig up the Layer Styles Window. Add three categories to the table by click on the + button. Click on the Gear Box and select Distribute with Natural Breaks (Jenks). Change the colors of each of the categories by clicking on the fill box and choosing a series of colors. See the map below for the final result.
