I walked around downtown Berkeley this morning and took photos of the aftermath of last night’s protests. It had been a predominately peaceful event. Out of 500+ protesters, only 4-5 individuals smashed windows, started fires, and looted local businesses. The ransackers sadly had nothing to do with the message of the protest. Rather, these were opportunists who camouflaged themselves among peaceful protesters to loot and vandalize under the cloak of anonymity granted by a large group.
It was a frustrating distraction away from the protest’s message, and it undermined the peaceful majority. Now the national news is focused on the destruction caused by a small group, instead of discussing the point of the protest. Do not conflate the protesters with the vandals – these were two separate groups out last night for different reasons.
Does food matter in neighborhood design? Should it? The answers to these questions are complicated and obscured by decades of perplexing policy and practice. There are many benefits of good food – that is, food which is healthy, affordable, fair, and sustainable. Proper nourishment has been linked in several studies to better classroom performance. Walkable access to healthy food can reduce America’s growing obesity and diabetes epidemics. Locally-sourced food can reinforce better dietary habits as consumers connect with the value chain and see eating as a more natural process.
The benefits are straightforward, but do most American neighborhoods actually support healthy food access?
The Department of City and Regional Planning at UC Berkeley has a rather arduous process for advancing to candidacy in the PhD program. It essentially consists of 6 parts:
Take all the required courses
Produce an inside field statement – a sort of literature review and synthesis explaining the niche within urban planning in which you will be positioning your dissertation research
Complete an outside field – sort of like what a minor was in college
Take an inside field written exam
Produce a defensible dissertation prospectus
Take an oral comprehensive exam covering your inside field, your outside field, general planning theory and history, and finally presenting your prospectus.
Whew. Lots to do this year. The good news is I am currently wrapping up my inside field statement and preparing to take the inside field exam. My topic is generally around complexity theory in urban planning. Here is the working abstract from my statement:
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:
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.
This guide was updated in June 2016 to reflect changes to the dependencies and the ability to install with Python wheels.
I recently went through the exercise of installing geopandas on Windows and getting it to run. 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 (also see this GitHub gist I put together).
It seems that pip installing geopandas works fine on Linux and Mac. However, several of its dependencies have C extensions that cause compilation failures with pip on Windows. This guide gets around that issue. For preliminaries, I have this working on Windows 7, 8, and 10. My Python environments are Anaconda, 64-bit, with both Python 2.7 and 3.5. I’m running geopandas version 0.2 with GDAL 2.0.2, Fiona 1.7.0, pyproj 22.214.171.124, and shapely 1.5.16.
This is a series of posts about visualizing spatial data. I spent a couple of months traveling in Europe this summer and collected GPS location data throughout the trip with the OpenPaths app. I explored different web mapping technologies such as CartoDB, Leaflet, Mapbox, and Tilemill to plot my travels. I also used Python and matplotlib to run some descriptive statistics and visualize other aspects of my trip.
My Python code is available in this GitHub repo. I also did some more involved work under the hood to prep the data and support these visualizations. For example, in the following posts I reverse-geocoded the spatial data set and reduced its size with clustering algorithms and the Douglas-Peucker algorithm:
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.
Lastly, I reduced the size of this spatial data set so Leaflet can render it more quickly on low-power mobile devices. I discussed why this is important and how to do it with the DBSCAN clustering algorithm and also with the Douglas-Peucker algorithm. The final data set I’ll be working with is available here.