USA Transportation and Urban Distribution

Lab Reports, Maps


Before coming to New York, I studied Industrial Design at Savannah College of Art and Design, which is an art college in Savannah, Georgia. One of the reasons I applied for Pratt was to experience the difference between life in US metropolis and small towns. So in this project, I chose the data of the distribution of USA urban and the layout of US transportation for visual analysis the urban layout.

Material & Method

The visualization software used in this project is Carto, the world’s leading location intelligence platform to make the map chart. You can upload the data sheets or use the data from Carto data library to form the map chart and in the next step you can set the chart attributes to achieve a better appearance. The data I used are Geometries for the USA Urban Areas/Clusters, Major Airport in the World and Railroads in the World.

Results & Analysis

USA Transportation and Urban Distribution

This is my final chart. The blue area indicates the distribution of cities in the United States, the green line is the distribution of railroads, and the red points are the distribution of airports. There are also some interesting discoveries during processing the data.

Major Airpot and Railroads in the World

First select to show the data of Major Airpot in the World, and Railroads in the World. The first map I used was Here Day. This map is suitable for displaying large-scale global layout, clearly showing continents and countries. It can be seen that the railways and airports in the developed regions of Europe and America are very densely distributed, especially the railway density in Europe is pretty amazing. In addition, India’s airports and railways are also very widely distributed. Compared Canada and Russia are relatively sparsely populated countries, so the number of railways and airport s are limited.

Geometries for the USA Urban Areas/Clusters

Then I zoom the map to the United States and changed the map to the Dark Matter, which can hide unnecessary geographic details well. Only the data of Geometries for the USA Urban Areas/Clusters is displayed. It is found that the urban of United States is mainly concentrated in the east country and west coastlines, and the cities in the central part are scattered and sparse.

Major Airport and City Clusters

A excellent function of Carto is that it can overlap different data layers to show the connection between different data set. Display the data of Major Airport and increase the point size. The chart shows that the distribution of urban areas and airports basically overlap.

USA Airports and Railroads

Use Intersect and Aggregate Analysis to find overlapping geometries, the Geometries for the USA Urban Areas/Clusters data as a target layer. In this way, the non-USA airports and railroads data can be removed to get the USA transport distribution. In addition to having an airport in each state, many airports will be built near the border of the two states. And most airports are near the railroads.

USA Airports and Railroads

Through Intersect and Aggregate Analysis filter, we can clearly see that the United States has many small islands on pacific and has airports built on them.

Finally, I opened the pop-up label of Major Airport to display the name of the airport and the corresponding Wikipedia link to help viewers better understand the specific information of the each airport.


Carto is a very easy-used map data visualization software, with a variety of high-quality maps and a user-friendly interface. However, some problems have been exposed during the process, such as the airport point size. When zooming in and out of the scale, the size will not automatically change well with the scale, resulting in poor visual effects. In addition, the automatically added white edge of data must be removed, especially in the case of black background, which could make the chart clearer.

Moreover, when there are many layers of data, the audience cannot hide the layers as they want in the final published chart, which reduce the using experience. For example, in this project, I used the urban distribution data of American cities, the data of world airports, and the data of world railway distribution to comprehensively analyze the layout of USA cities and infrastructure. But if the final chart only can show all the data layer in the chart at the same time, it will be very messy, while user cannot hide some of them.