Wednesday, June 21, 2017

Module 4 - Visisbility Analysis



This week's lab tasked us with several deliverables one of which required us to conduct a visibility analysis of the finish line for the Boston Marathon.  Specifically, we were required to examine the viewshed and determine where best to place two additional cameras for optimal surveillance coverage.  I placed the two additional cameras on tops of buildings and created an offset of 100 using the field calculator in the attribute table.  I first placed a camera in the lower right corner of the map on top of a building but it was angled the wrong way and looking down the street in the wrong direction.  I could not figure out how to change the angle of the cameras so I placed another camera on the opposite side of the street from the existing camera. Part of the viewshed for this camera appears to be over tops of the buildings in the opposite direction of the finish line but part of it does appear to be on the finish line. I will have to revisit how to adjust the angle but I had to move on due to time constraints.  I placed the other camera at the other end of the street on top of a building.  The screenshot below shows the Boston Marathon finish line with a red X mark and the three cameras appear on rooftops with the corresponding viewshed shown in green.




Wednesday, June 7, 2017

Lab 2 - Least Cost Path Analysis and Corridor Analysis


This week our lab covered finding the optimal path for a new pipeling using the least cost distance and least cost path tools.  I struggled with this lab and was only able to complete Scenario 1 of Part A, determining the number of river crossings, which are shown in the screen shots below.  

I spent a considerable amount of time (18 hours at least spent on this lab) trying to figure out the next Scenarios but continued to get an error messages on all of my attempts. I tried multiple times using different inputs and back list rasters but nothing worked.  Although I realize this is an applications class, it would have been helpful to have had some detailed steps or a tutorial since I have never done any of these types of GIS analyses before.  I am unable to finish part B (Black Bear Corridor) because of time constraints due to work.    







Wednesday, May 3, 2017

Final Project




The purpose of this project is to cartographically represent the mean SAT scores and test participation rates for 2014 college bound seniors to supplement a Washington Post article on high school seniors and college entrance exams.  The creation of a map for the Washington Post provides a visual representation of both mean SAT scores and test participation rate which allows the audience to better analyze state rank. 

A shapefile of the U.S. State boundaries was downloaded from the U.S. Census Bureau to use as a background for the data.  The map was re-projected to North American Albers Equal Area Conic to preserve area.  The inset maps for Alaska and Hawaii were each re-projected to projections appropriate to each state:  Alaska Equal Area Conic and Hawaii Equal Area Conic, respectively.  Mean SAT Scores and test participation rates for the year 2014 were obtained from the College Board website.  The mean test scores and test participation rates were compiled into an excel spreadsheet to export into ArcMap.  Specifically, the scores obtained include mean math, mean critical reading and mean writing scores for the individual states.  The three scores for each state were added together to obtain the final test mean.  The excel spreadsheet was then exported into ArcMap and joined with the attribute table of the US State boundary map.  The state “name” in each excel spreadsheet was the common field used to join the two files.

Once the spreadsheets were joined, the data was then symbolized two different ways to create a bivariate map that visually represents both sets of data: mean SAT scores and test participation rates.  The mean SAT scores were displayed with a choropleth mapping technique utilizing a graduated color scheme selected under “quantities” in the “Symbology” tab.  The data was categorized into five classes and classified using natural breaks classification.  This allows for the difference in data values in the same class to be minimized, and in turn, maximizes the difference between the classes which provides a more accurate portrayal of trends in the data.  

The participation rate was displayed with a graduated symbol technique by selecting graduated symbols under “categories”, in the “symbology” tab.  Manual classification was used to classify the data into six different classes.  Six classes allowed for better grouping of the data and made it easier to interpret.  Five classes created data classes that were just too large for easy analysis.   

 Once the data classification was added to the base map, two inset maps for Alaska and Hawaii were added.  Additionally, an inset map of the District of Columbia, Maryland and Delaware was also added to enlarge the area which was difficult to see because of the small geographic area that was populated with numerous symbols.  The aforementioned steps for each symbology technique (graduated colors and graduated symbols) were followed for each inset map.  The essential map elements were then added to the map including a north arrow, legend, neatline, and scale bar.  

The map was then exported to Adobe Illustrator to add the finishing touches of a background color, title, cartographer, date, data source and map summary.  Gestalt’s figure-ground principle was employed with a drop shadow effect added to the Alaska and Hawaii inset maps which allows them to stand out on the page and brings them closer to the viewer. A drop shadow effect and different background color were added to the map summary to enhance the appearance and to also allow it to stand out.  A border was placed around the District of Columbia, Maryland and Delaware inset map to distinguish it from the main map.  The New England area was separated from the rest of the map because the geographic region was also congested with “percent participation” symbology.  Setting it apart allows the viewer to more easily distinguish the features.

I have really enjoyed Cartographic Skills and am excited about what I have learned.  I hope to continue to use GIS and improve on what I have learned in this class.



Wednesday, April 19, 2017

Module 12 - Google Earth


This week's Module focused on the future of cartography, neocartography, Volunteered Geographic Information and Google Earth.  Google Earth is a great way to share information which is accomplished by creating a KML file.  Points of interest and points of view can be saved in a KML file and also made into a video tour. KML clients such as ArcGISOnline, ArcGISExplorer, ArcGlobe, and GoogleEarth can read KML files. The lab required us to create a shareable web map using our dot density map from Module 10 and also an interactive google tour.

To complete the lab, I converted my Dot Density map from Module 12 to a kml file.  Once the dot density map was converted to kml, I was able to open it in Google Earth.   I was able to create the video tour once my map was open in Google Earth by adding placemarks to the cities required in our lab. I think this is a really cool feature of GIS and was very excited to learn about it this week.

  

Sunday, April 9, 2017

Module 11 - 3D Mapping


This week's module covered 3D mapping techniques which were accomplished using ArcGIS, ArcScene, ArcGlobe and Google Earth.  We first completed the ESRI training course "3D Visualization Techniques Using ArcGIS" which included the topics: Setting base heights for feature and raster data, vertical exaggeration, illumination and background color (including the importance of light and shade effects in 3D mapping) and extrusion.  The next task in the lab module required us to convert 2D building footprints into 3D and share on Google Earth.  I had trouble with the Google Earth portion of this lab because of the difficulty I had trying to open my map.  Regardless of the frustration that I had, I did enjoy learning about 3D mapping capabilites.