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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. 



Analysis with Live Maps: Mapping the Location of Important Naval Ships

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. 


Creating Elevation Contour Maps from Digital Elevation Models

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. 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.


Street Segments as Units of Analysis: Spatially Joining Points and Lines

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.