Good day all,
This post is dedicated to this weeks Cartography assignment exploring 3D Mapping. A number of applications were used in this, to include ArcMap, ArcScene, ArcGlobe, and Google Earth. The data for the lab was provided from ESRI.com for a training session produced by ESRI, as well as information provided from UWF. Some of the overall objectives for this week were too describe or list data types and applications for performing techniques related to creating 3D maps.
The lecture this week was largely provided through ESRI related products, from a lecture provided to one of their user conferences and a White Paper discussing 3D mapping. Lets hit on some of the key pieces of information from the training before going into some of the applications used this week. 3D data is created by adding a third element to a normal Cartesian (X,Y) or grid based point location commonly referred to as the Z-value. This is most typically used to reference height above or below a surface. Buildings usually have a positive Z value referencing the height of the building, whereas wells extend below ground with a negative Z-value for their depth. The surface mentioned here also has to originate in some particular form for our applications. These are usually found in raster data or a triangulated irregular network (TIN); think many points across a plain all connected via triangles, where each point and location between points or on the triangle face can have a determined Z value. Now understanding this let us look at some of the basic applications performed in this weeks training.
The above is a converted 2D image based on the Z value for each cell in the raster of Crater Lake in Oregon. Ive taken the data and also given it a 2x exaggeration to enhance the 3D visual scene depth just for this snapshot. You can see the water layer for the lake as well as streams in the area. All of these layers have been provided the appropriate Z value to sit on or in the appropriate location for this data set.
This picture started as a TIN (described above) to provide the surface to drape the aerial photo over top of, which then in turn had the buildings extruded to match their overall height and footprint. These extruded buildings could then have any number of actions performed on or about them. Imagine we could change their colors to reflect what type of building, commercial, industrial, residential area they belong too, or anything else you could think of to denote one building type vs another.
Here is another application for 3D maps where the height portrayed does not reflect actual building height. We are depicting this area by two criteria; the first being the color code which represents different zoning types, and the height which represents the overall value of the representative building. The higher the block, the more the building is worth. You could use the same principle to apply to population by census tract or other enumeration unit.
Overall 3D mapping applications can be used for as many things as you can imagine, though some of the most common now are for areal simulation, urban planning, transportation network, real estate, and census. These are fast evolving into more and more common applications, particularly with off the shelf software such as Google Earth. A few of the only disadvantages to the 3D map are the time to produce and sometimes cost of material, software, and data to create the map. However these maps are inherently the wave of the future. I hope you've enjoyed this scratching of the surface of 3D maps.
This post is dedicated to this weeks Cartography assignment exploring 3D Mapping. A number of applications were used in this, to include ArcMap, ArcScene, ArcGlobe, and Google Earth. The data for the lab was provided from ESRI.com for a training session produced by ESRI, as well as information provided from UWF. Some of the overall objectives for this week were too describe or list data types and applications for performing techniques related to creating 3D maps.
The lecture this week was largely provided through ESRI related products, from a lecture provided to one of their user conferences and a White Paper discussing 3D mapping. Lets hit on some of the key pieces of information from the training before going into some of the applications used this week. 3D data is created by adding a third element to a normal Cartesian (X,Y) or grid based point location commonly referred to as the Z-value. This is most typically used to reference height above or below a surface. Buildings usually have a positive Z value referencing the height of the building, whereas wells extend below ground with a negative Z-value for their depth. The surface mentioned here also has to originate in some particular form for our applications. These are usually found in raster data or a triangulated irregular network (TIN); think many points across a plain all connected via triangles, where each point and location between points or on the triangle face can have a determined Z value. Now understanding this let us look at some of the basic applications performed in this weeks training.
The above is a converted 2D image based on the Z value for each cell in the raster of Crater Lake in Oregon. Ive taken the data and also given it a 2x exaggeration to enhance the 3D visual scene depth just for this snapshot. You can see the water layer for the lake as well as streams in the area. All of these layers have been provided the appropriate Z value to sit on or in the appropriate location for this data set.
This picture started as a TIN (described above) to provide the surface to drape the aerial photo over top of, which then in turn had the buildings extruded to match their overall height and footprint. These extruded buildings could then have any number of actions performed on or about them. Imagine we could change their colors to reflect what type of building, commercial, industrial, residential area they belong too, or anything else you could think of to denote one building type vs another.
Here is another application for 3D maps where the height portrayed does not reflect actual building height. We are depicting this area by two criteria; the first being the color code which represents different zoning types, and the height which represents the overall value of the representative building. The higher the block, the more the building is worth. You could use the same principle to apply to population by census tract or other enumeration unit.
Overall 3D mapping applications can be used for as many things as you can imagine, though some of the most common now are for areal simulation, urban planning, transportation network, real estate, and census. These are fast evolving into more and more common applications, particularly with off the shelf software such as Google Earth. A few of the only disadvantages to the 3D map are the time to produce and sometimes cost of material, software, and data to create the map. However these maps are inherently the wave of the future. I hope you've enjoyed this scratching of the surface of 3D maps.
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