Module 11: 3D Mapping

The purpose of this lab was to explore and visualize data in 3D scenes and maps. 3D maps and scenes can be helpful for visualizing the real world. They also allow us to ask and analyze different questions, such as the effect of date and time of date on illumination and shade in an area. Sometimes investigating a certain set of data in 3D allows us to have a different perspective or gain different and new insights that cannot be answered in a 2D setting. Applications for 3D maps are endless, including showing the impact of a new building in an area, realistically displaying a transportation route through a city, over a country or even globally, or even visualizing subsurface features such as wells, pipelines or fault lines. Although 3D maps have wide ranging applications and they suffer from the same pitfalls as 2D maps, navigating them can be initially cumbersome or difficult and sometimes they can be more difficult to interpret initially, depending on the data being displayed.

There were several exercises and data sets to explore in this lab. For this lab, I explored data for Crater Lake in Oregon, linking a 2D and 3D scene in ArcGIS Pro. The features of Crater Lake were more impactful in a 3D scene than the 2D scene.

Then I explored data for downtown San Diego and symbolized it cartographically (above) and photorealistically, bookmarking two locations in ArcGIS Pro. The purpose of the map above was to locate a hotel to a attend a conference with the following specifications: ocean view, near a shaded park, and near retail. Because the buildings and other features did not have to be realistic and part of the search criteria was building type, I symbolized the features cartographically instead of photorealistically. I extruded the buildings based on the number of stories and symbolized them by usage. I symbolized the parks were symbolized with a a grass fill and the trees by genus. Finally, I adjusted the illumination the date and time of the conference. From this map, I can now easily select a hotel based on the original criteria.

Finally, I created 3D data for buildings in downtown Boston in ArcGIS Pro, shared it as a KML file and viewed it using Google Earth. I derived the height or Z value of the buildings from raster data (from Lidar data) from the MassGIS Bureau of Information using the Create Random Points and Add Surface Information tools. I joined the resulting table to the feature class by using the Add Join tool and then created a KML layer using the Layer to KML tool. Although the final KML file was visible in Google Earth, there were some glitches initially with visualizing the feature in 3D.