Categories
Data

Chaos Theory and the Logistic Map

Logistic map bifurcation diagram showing the period-doubling path to chaosUsing Python to visualize chaos, fractals, and self-similarity to better understand the limits of knowledge and prediction. Download/cite the article here and try pynamical yourself.

Chaos theory is a branch of mathematics that deals with nonlinear dynamical systems. A system is just a set of interacting components that form a larger whole. Nonlinear means that due to feedback or multiplicative effects between the components, the whole becomes something greater than just adding up the individual parts. Lastly, dynamical means the system changes over time based on its current state. In the following piece (adapted from this article), I break down some of this jargon, visualize interesting characteristics of chaos, and discuss its implications for knowledge and prediction.

Chaotic systems are a simple sub-type of nonlinear dynamical systems. They may contain very few interacting parts and these may follow very simple rules, but these systems all have a very sensitive dependence on their initial conditions. Despite their deterministic simplicity, over time these systems can produce totally unpredictable and wildly divergent (aka, chaotic) behavior. Edward Lorenz, the father of chaos theory, described chaos as “when the present determines the future, but the approximate present does not approximately determine the future.”

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Categories
Planning

LEED-ND and Neighborhood Livability

I recently co-authored a journal article titled LEED-ND and Livability Revisited, which won the Kaye Bock award. LEED-ND is a system for evaluating neighborhood design that was developed by CNU, USGBC, and NRDC. Many of its criteria, particularly site location and neighborhood pattern, accordingly reflect New Urbanist and Smart Growth principles and are inspired by traditional neighborhood design.

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Categories
Academia

The Inside Field Exam and Urban Complexity

I recently completed my inside field exam, one of the many steps involved in advancing to candidacy. The three professors on your inside field committee send you six questions – a pair per professor – and you are given 72 hours total to answer one question from each pair. The answers are to be in the form of a scholarly article with thorough citations. Long story short, you’ve got to write 30 pages of academic scholarship in three days.

The exam questions themselves are very interesting. The professors construct them based on their reading of your inside field statement, trying to probe areas that might be particularly rich or a bit weak in the statement. Here are the questions I answered:

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Categories
Urban

Aftermath of the Berkeley Protests

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.

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Categories
Planning

How Our Neighborhoods Lost Local Food (And How They Can Get It Back)

Healthy-foodThis post is adapted from an article I wrote in Progressive Planning.

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?

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Categories
Planning

Urban Complexity and the March Toward Qualifying Exams

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:

  1. Take all the required courses
  2. 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
  3. Complete an outside field – sort of like what a minor was in college
  4. Take an inside field written exam
  5. Produce a defensible dissertation prospectus
  6. 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:

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Categories
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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Categories
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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Categories
Data

Visualizing Summer Travels

projected-shapefile-gps-coordinatesThis 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.

Here is the series of posts:

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:

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