3D Mapping: Buildings in Boston

After completing the Esri Training Course "3D Visualization Techniques", we were asked to convert 2D features to 3D features using data gathered from LiDAR.

First, the Esri course taught us the vocabulary of 3D - multipatches, Z-values, terrain data sets, TINs, rendering, and extrusion, for example.  After examining the elements of 3D data, we learned how to define base heights, then set about practicing these skills using a map of Crater Lake National Park.  Base height was set for various data types, including an elevation raster, a 3D feature class, a line feature class, a point feature class, and a raster vegetation layer.  Next we learned how to enhance 3D views using Vertical Exaggeration and Illumination.  In the last section we learned how to generate 3D Objects from 2D Objects.  In the exercise, we extruded Buildings and Wells, then added an aerial photo draped over an elevation TIN.  Another step was Extruding Parcel Values so that these differences would be reflected in the height and color of the buildings.

This course provided the skills necessary to complete the final part of the lab, in which a map with a 2D shapefile of buildings in Boston was converted into the 3D map above.  After the two layers were added (the raster base layer and the shapefile buildings layer), the base height was calculated.  Since the raster layer had the elevation information, a new layer was created with random points for each building which had the elevation information added to them. The Mean Z Value was calculated in the SamplePoints layer, then joined to the buildings layer. Once the 2D data had "Z" values, it was possible to create a 3D map by extruding the building features.  For people without access to ArcGIS, these maps can be saved as a .kml file  and can be viewed instead using Google Earth, for example. 

Maps created in 3D are quite attractive when done well.  The example map of Napoleon's Moscow campaign is a fantastic example of how easy and fun it is to view and interpret one of these maps when it is done well.  Being able to fly around, zoom in, explore in a virtual world is exciting.  Still, at this point the ability of most people to access these maps is limited, and there seems to be the potential for bad data and poorly created maps, as there are in 2D.  That said, it seems clear to me that the abilities of a 3D map to convey information clearly, quickly, and accurately, are far above those of 2D maps.

Vector 2 Lab: Buffers and Overlays

This week we created a map of possible campsite locations within the De Soto National Forest while being introduced to buffering and overlaying.  These tools are commonly used, and are key to the process of answering questions about locations using specific criteria.

Buffering was used to find areas which satisfied two criteria for possible campsites - being within a certain distance from water features and roads.  A distance of 300m was set as the buffer for roads, 150m for lakes, and 500m for rivers. 

Overlays were used to join layers using one of six possible tools.  The "Union" tool was used to join the two buffered layers from the previous step.  This created a new layer showing areas which were within both the road buffer area and the water buffer layers.

The next step was to exclude conservation land.  This was done by using the overlay tool "Erase", which selected particular areas to remove from the "possible campsites" layer.  This was a multipart layer which we converted to a singlepart layer so that individual records could be accessed and manipulated within the Attribute Table.  In the lab, we looked at the attribute "Area" to see which possible campsites had the largest and smallest area. The total area in square meters was also calculated.

These two tools are clearly very powerful and useful in a large variety of situations, in many industries and occupations.  I'm excited to explore ways in which they are used in archaeology.

Dot Mapping South Florida's Population

 

 

 

Dot density mapping uses raw total data to display distribution patterns.  After carefully choosing an appropriate size and value, dots are distributed on the map in such a way that they most accurately reflect actual distribution on the ground.  Limitations can be placed on the data so that dots are not displayed in areas where they wouldn't actually be.  For example, population dots in the map above are not placed in lakes, ponds, marshes, etc.  Instead they are confined to areas selected specifically to match the type of data (population) that is the focus of the map - in this case, urban areas are an attribute that relates to population density and distribution, so using a mask, the dots were only included in urban areas. 

 

Dot size and value are also important aspects of making a map of this type.  The goal is to display the data in a way that enhances understanding of the data in an accurate way.  When the dots are too large or too small, patterns can be missed or misinterpreted.

 

This method is especially suited to some types of data.  The text illustrates this with maps of wheat harvests, and the map above with population data.  I have also seen it used successfully in archaeological contexts, and I hope to learn more about this in future courses.

Flow Line Mapping

Lab 9: Map of Immigration to the U.S. using Flow Lines

 

 

This week we were asked to make a map in Corel Draw.  The focus of the lab was Flow Lines.  After choosing one of two base maps already made for us in ArcMAP,  I added curved lines using the Bezier tool, then modified them so that they fit the main criteria - not interfering with the features underneath.  To do this, I made them transparent.  I combined this effect with line colors to match each region.  The exception was Asia, which has a grey line  - yellow seemed too light and too similar to the green of Oceania's line.  Arrows were added to one end of the flow line and resized to fit. 

 

In Excel, a formula was used to find the proportional width of each flow line based on the number of immigrants coming from each region.  This way the flow lines visually represent not only direction of movement, but the number of people moving.

 

The last step was to create a legend and all the other usual map elements, and to experiment with some of the stylistic effects available in Corel.  Options included changing color schemes for the regions and U.S. States, making the flow lines transparent, and using the Drop Shadow, Extrude, and Bevel tools.  I chose to make my lines transparent and to use Drop Shadow on the map title. 

 

I think the most useful part of this lab was learning how to use the Bezier tool to make curved lines.  It isn't easy, in my opinion, and it took a lot of trial and error to get the 6 on my map to come out the way they did, but it will be a good skill to develop as curved lines are so common on maps.  I also struggled at first making the choropleth map legend, and at the end I had learned how to use the eye dropper to match fill color, and had worked with the rotate feature enough to get the hang of it.

 

All in all, it was a productive week and a fun, challenging lab.

Data Search Lab: Palm Beach

Palm Beach County, Florida...

 

 

The results of two weeks' lab work are displayed above.  If someone had told me I'd be able to do this after 8 weeks of study, I wouldn't have believed them.  If I had another couple of weeks, the maps would look better  - but all things considered, I'm quite proud of what I accomplished.

 

This week's lab asked us to search for specific types of data, download the data, put all data in the same projection, clip it to be within a specified county (Palm Beach in my case), and then make everything look sharp.  Some of the data was vector, some was raster.  Some came from Labins, some from FGDL, some from USGS.  One thing that struck me during this process - there is a TON of data out there, and it's quite interesting to check it all out.  There will come a time when I have the luxury of doing that, but not right now!

 

The repetition of adding data, checking out the metadata, reprojecting, and clipping made me really understand the process.  I don't have to look through old lab notes to do it any more, and I call that progress. 

 



Precipitation Maps

The isarithmic precipitation maps we learned how to produce this week are familiar to most people, and it was interesting to see how one is put together after having looked at them so many times on the nightly weather report. 

We worked with raster data from the USDA that was interpolated using the PRISM Interpolation Method devised by people at Oregon State University.  Using the Spatial Analyst Tool we created two maps showing annual precipitation in Washington state.  One map shows the data as a continuous spread of color in a spectrum.  The transition between one tone and another isn't perceptible.  The other map displays the data with hypsometric tinting which creates a stepped appearance to the color spectrum.  Hillshade Effect was added to both maps to display the map in relief. This week we also learned how to make contour lines, using the Spatial Analyst tool "Contour List" where the values for each index contour line were entered, and then had a choice of which map should have contours added to it.

 

 



This was a fun lab, and I can see how useful this technique will be going forward.