Tag: maps

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Data

World Population Projections

Batman and Robin: By 2050, 70% of the world's population...The U.N. world population prospects data set depicts the U.N.’s projections for every country’s population, decade by decade through 2100. The 2015 revision was recently released, and I analyzed, visualized, and mapped the data (methodology and code described below).

The world population is expected to grow from about 7.3 billion people today to 11.2 billion in 2100. While the populations of Eastern Europe, Taiwan, and Japan are projected to decline significantly over the 21st century, the U.N. projects Africa’s population to grow by an incredible 3.2 billion people. This map depicts each country’s projected percentage change in population from 2015 to 2100:

UN world population projections data map: Africa, Asia, Australia, Europe, North America, South America

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Data

The Landscape of U.S. Rents

Which U.S. cities are the most expensive for rental housing? Where are rents rising the fastest? The American Community Survey (ACS) recently released its latest batch of 1-year data and I analyzed, mapped, and visualized it. My methodology is below, and my code and data are in this GitHub repo.

This interactive map shows median rents across the U.S. for every metro/micropolitan area. Click any one for details on population, rent, and change over time. Click “switch” to re-draw the map to visualize how median rents have risen since 2010:

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Data

Exporting Python Data to GeoJSON

I like to do my data wrangling and analysis work in Python, using the pandas library. I also use Python for much of my data visualization and simple mapping. But for interactive web maps, I usually use Leaflet. There isn’t dead-simple way to dump a pandas DataFrame with geographic data to something you can load with Leaflet. You could use GeoPandas to convert your DataFrame then dump it to GeoJSON, but that isn’t a very lightweight solution.

So, I wrote a simple reusable function to export any pandas DataFrame to GeoJSON:

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Academia

Urban Informatics and Visualization at UC Berkeley

The fall semester begins next week at UC Berkeley. For the third year in a row, Paul Waddell and I will be teaching CP255: Urban Informatics and Visualization, and this is my first year as co-lead instructor.

This masters-level course trains students to analyze urban data, develop indicators, conduct spatial analyses, create data visualizations, and build Paris open datainteractive web maps. To do this, we use the Python programming language, open source analysis and visualization tools, and public data.

This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving. We don’t require any prior programming experience – we teach this from the ground up – but we do expect prior knowledge of basic statistics and GIS.

Update, September 2017: I am no longer a Berkeley GSI, but Paul’s class is ongoing. Check out his fantastic teaching materials in his GitHub repo. From my experiences here, I have developed a course series on urban data science with Python and Jupyter, available in this GitHub repo.

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Data

Map Projections That Lie

How big is Greenland? It’s huge, right? At 836,109 square miles in size, Greenland is the largest island and the 12th largest country on Earth. With only 56,000 people living in that enormous area (80% of which is covered by the world’s only extant ice sheet outside of Antarctica), it is also the least densely populated country on Earth.

You can get a sense of how large Greenland is when you look at a map of the world:

world map mercator projection

It’s huge! Greenland is bigger than the entire continent of Africa! Or is it? The map above uses the common Mercator projection to project the 3-D surface of the Earth onto a 2-D surface suitable for a paper map or an image on your computer screen. But it’s not easy to project the curved surface of a sphere onto a rectangular plane. Compromises must be made. In the case of the Mercator projection, the compromise is that objects’ sizes become increasingly distorted the further they are from the equator. At the poles, the scale and distortion become infinite.

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Planning

Visualizing Craigslist Rental Listings

Our paper on collecting and analyzing U.S. housing rental markets through Craigslist rental listings has been accepted for publication by the Journal of Planning Education and Research. Check out the article here. This map of rental listings in the contiguous U.S. is divided into quintiles by rent per square foot:

Map of 1.5 million Craigslist rental listings in the contiguous U.S., divided into quintiles by each listing's rent per square foot
Map of 1.5 million Craigslist rental listings in the contiguous US, summer 2014

Categories
Data

Visualizing Summer Travels Part 6: Projecting Spatial Data with Python

This post is part of a series on visualizing data from my summer travels.

I’ve previously discussed visualizing the GPS location data from my summer travels with CartoDB, Leaflet, and Mapbox + Tilemill. I also visualized different aspects of this data set in Python, using the matplotlib plotting library. However, these spatial scatter plots used unprojected lat-long data which looked pretty distorted at European latitudes.

Today I will show how to convert this data into a projected coordinate reference system and plot it again using matplotlib. These projected maps will provide a much more accurate spatial representation of my spatial data and the geographic region. All of my code is available in this GitHub repo, particularly this notebook.

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Data

Using geopandas on Windows

projected-shapefile-gps-coordinatesThis guide was written in 2014 and updated slightly in November 2020.

I recently went through the exercise of installing geopandas on Windows. Having learned several valuable lessons, I thought I’d share them with the world in case anyone else is trying to get this toolkit working in a Windows environment. It seems that pip installing geopandas usually works fine on Linux and Mac. However, several of its dependencies have C extensions that can cause compilation failures with pip on Windows. This guide gets around that issue.

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Data

Visualizing Summer Travels Part 5: Python + Matplotlib

This post is part of a series on visualizing data from my summer travels.

I’ve previously discussed visualizing the GPS location data from my summer travels with CartoDB, Leaflet, and Mapbox + Tilemill. Today I will explore visualizing this data set in Python, using the matplotlib plotting library. All of my code is available in this GitHub repo, particularly this notebook.

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Data

Visualizing Summer Travels Part 4: Mapbox + Tilemill

This post is part of a series on visualizing data from my summer travels.

I’ve previously discussed my goals in visualizing GPS data from my summer travels and explored visualizing the data set with CartoDB and with Leaflet. The full OpenPaths location data from my summer travels is available here and I discussed how I reverse-geocoded it here.

Mapbox is a major provider of online web mapping services such as tiled web maps, the Tilemill cartography IDE, and the mapbox.js javascript library. Today I’ll run through how to create an interactive data map in Tilemill’s design studio, export the map as a set of tiles, upload the tileset to Mapbox, and then use a javascript client to display the map on a web page. Our final result will look something like this:

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