Cartographic Skills – Week 11 – 3D Mapping

GIS 3015: Cartographic Skills

Week 11 Lab: 3D Mapping

This week we explored 3D mapping and particularly the conversion of 2D features into 3D features by using elevation data from other datasets with the same extent.  Of particular interest to me, I learned to convert 2D statistical or informational data such as land use codes, property value, number of murders, population and similar into 3D features for better communication and presentation.

Advantages of 3D mapping include the ability to show vertical information such as attributes for floors in a multi-story building.  3D maps are also more intuitive thus placing less reliance on the legend and 3D maps can be navigated – how cool is that? 

Disadvantages of 3D mapping include having difficulty in navigating, such as getting lost or disoriented – which way is north?  In addition, map content can become hidden as large 3D features may obstruct the view of smaller features thus information getting lost or miscommunicated by the reader.

The lab activities included the setting of base heights for raster and feature data so data containing elevation is shown in 3D perspective, setting vertical exaggeration to better communicate information by amplifying feature heights, setting illumination and background color for effect, extruding features to stretch 2D features vertically, and converting a building foot print layer into a KML file for viewing in Google Earth (shown below).

Intro to GIS – Week 11 – Vector Analysis 2

GIS 4043 Week11 Lab: Vector Analysis 2

This assignment involved the use of ArcPython, Buffer, Overlay (Union, Intersect, Erase) tools, and the Multipart to Singlepart tool to identify potential campground sites at the De Soto National Forest.

The map below depicts potential campground sites within 300 meters of a road, 150 meters of a lake, 300 meters of a river, and not within a conservation area.

Cartographic Skills – Week 10 – Dot Mapping

GIS 3015: Cartographic Skills

Week 10 Lab: Dot Density Mapping

The Dot map below depicts the population density of the counties in Southern Florida.  Each dot represents a population of 25,000 thus it is possible to count dots to recover the original data and determine the aggregate population for each county.  The dots have been placed only on urban areas where people reside and not on surface waterbodies depicted.  The map was completed in ArcGIS.         

Cartographic Skills – Week 9 – Flow Line Maps

GIS 3015: Cartographic Skills

Week 9 Lab: Flow Line Maps

The Flow Line map below depicts immigration to the United States in 2007 from various regions around the world and the percentage range of immigrants per State in the inset map.  The map below combines a Flow Line map with a Choropleth map (the inset).

The map was created in CorelDraw.  The widths of the flow lines and arrows are proportional to immigration by region and the proportionality was calculated in Excel using the number of immigrants by region.  Merging flow lines maintain the width proportionality. 

The map incorporates a couple of visual effects including a drop shadow for the title and a fountain fill for the inset map and the legend.

Intro to GIS – Weeks 7 and 8 – GIS Data Search

GIS 4043 Weeks 7 and 8 Lab: GIS Data Search

This assignment involved the clipping of vector (clip tool) and raster (data frame clipping) data to a county boundary, the searching/acquiring online of GIS data and the re-projecting of all datasets to a common projected/geographic coordinate system.  I was assigned Miami-Dade County.  The assignment involved five (5) vector datasets, two (2) environmental-related datasets, and two (2) raster datasets (DOQQ and DEM/NED).

Below is are my three (3) maps showing the county boundaries, cities/towns, public land (parks), major roads, major rivers, waterbodies, strategic habitat conservation areas, wetlands, a DOQQ of the Miami area, and the elevation DEM/NED for Miami-Dade County.

Cartographic Skills – Week 8 – Isarithmic Mapping

GIS 3015: Cartographic Skills

Week 8 Lab: Isarithmic Mapping

The Isarithmic maps below depict the 1981-2010 average annual precipitation for Washington State.  Isarithmic maps depict smooth, continuous phenomena across an area using varying symbology methods such as the continuous tone with hillshade effect and hypsometric tints with contours and hillshade effect shown below.  The data was measured at control points and then interpolated using the Parameter-elevation Relationships on Independent Slopes Model (PRISM). 

As precipitation generally increases with elevation, PRISM incorporates elevation into the surface by utilizing a digital elevation model (DEM).  PRISM has created substantially improved climate data by utilizing a relatively dense monitoring network with nearly 13,000 precipitation stations over the 48 conterminous states.