Writing about cities, urban planning, mapping, data visualization & creative coding
The City of Calgary estimates there are almost 7 million trees growing within city limits. The urban forest we see today is the result of over a century of careful planning, planting and maintenance. Using a dataset of public trees, I did a short exploratory data analysis to understand the distribution of tree species.
With the launch of Calgary’s new Bus Rapid Transit (BRT), I experimented with designing an interactive transit map to visualize the new system. The challenge involved creating an accessible, intuitive map where a user could quickly see the new routes and explore connections.
Transit agencies must influence how their transit maps are displayed online. By relinquishing the design and mapping to tech companies, they lose out on the opportunity to communicate effectively with their customers.
Decoding GTFS Realtime transit data can be a challenging endeavour for simple front end development. By using protobuf.js, AWS Lambda and Serverless, we can make the data accessible for web application and visualizations.
With the recent release of a traffic incident data set in Calgary, I was interested in utilizing the data to create a simple prediction model using machine learning. The availability of traffic incident data is still lacking in parts of Canada and its release can create an opportunity to analyze, interpret and predict urban transport behaviours.
A simple traffic collision predictor and visualization created using Keras, tensorflow.js, React and Mapbox.
I’m a little late posting a follow up to my original article about using Machine Learning, specifically Deep Learning to help segment and classify streetscapes. It was actually the release of Google’s Deep Lab V3+ Convolutional Neural Network (CNN) in March 2018 that drew me back to this topic. Here is the final result of evaluating pedestrian routes in the Ramsay neighbourhood of Calgary.
Urban farming offers cities both economic and social benefits. With increasing populations migrating to urban centres, the development of sustainable, secure foods sources is a boon, however the potential for soil contamination is a barrier.
How do we know if transit routes are traveling through the most connected areas of a City? If we were to map bus routes and network centrality for communities what would that tell us about transit planning? Continuing on from my post about Social Street Networks, I’ve created an interactive map showing Calgary Transit bus routes and closeness centrality measures of the neighbourhoods they intersect.
I’m always excited about discovering new ways to visualize how cities are planned and how street networks impact mobility and use. Recently I came across the study of Network Science and Social Network Analysis and its application to Transportation Planning.
Through my research on the basic operations of Artificial Neural Networks (ANN) and deep learning I’ve experimented with Caffe and a (somewhat) dated program called Segnet. Although it was developed in 2015 I was intrigued by the application of pixel wise segmentation of streets.
This is a simple sketch of 3D arcs using bezier curves. They can be a little tricky to figure out, especially when you can’t see the handles. I’ve been using them for a while in photoshop and have a pretty good grasp on how to manipulate them. I might try implementing this in a mapping visualization. Here is the code.
I’ve been a little obsessed with artificial neural networks recently. A couple weeks ago I came across some research by the Machine Intelligence Lab at Cambridge which led to a segmentation program called SegNet. The accuracy of predicting and segmenting streetscape features had me curious about how the program worked.
Continuing with my 3D mapping theme, I’ve built onto my previous map with GeoJSON road data from the City of Calgary Open Data catalogue.
I recently took a Environmental Engineering graduate course about contaminant transport. Part of the course involved finite difference analysis and modelling so I thought I’d do a simple visualization with Processing to see the equations in action. The coding part was pretty simple but it was interesting to see how I could apply Processing to visualize a contaminant plume.
Since the City of Calgary began releasing open data through the online catalogue I’ve been trying to come up with an interesting way to represent and visualize census data. It’s often difficult to represent something as boring as population and demographic data in a compelling way. The City of Calgary’s own census page presents the data in pages and pages of tables and graphs.
Although using mapping application like Google Maps, Mapbox or Leaflet can be extremely useful, I was looking for a way to draw simple GeoJSON files in my Processing/p5.js sketches. I didn’t want to use a library to integrate my GeoJSON files and I wanted more flexibility in how my maps could be drawn and manipulated. I came across this post by Mike Fowler that achieved exactly what I was considering so I went about implementing this method in p5.js.