BEST Commuter Challenge 2015

This is the third year I have mapped the travel patterns by mode from the BEST Commuter Challenge. This year I also decided to create custom maps showing results for the 104 offices located in the lower mainland, so if you want to see your custom results, send me a quick email to Anth42[at]gmail.com and I'll send you a map like the Golder Associates example at the bottom of this post. Thanks for visiting and keep up the sustainable commuting!

Read More »

Personal Google Location History

Do you know where you have been? Google does. That is if you have your location history turned on. I personally love this feature, it means if you forget your phone somewhere you can log in to any computer and map the phone location, and even lock or erase the phone contents remotely.

I have been thinking about mapping my own personal history for some time, so I finally took a few minutes to download the past seven months of life, since I moved to Toronto. The files are extracted as monthly KML files from my personalized Location History portal. I then merged these into a single file in ArcMap. Because the track lines are fairly random at times, I transformed the vertices into points representing the exact locations where Google actually called home. This represented about 150,000 points (21,000 points per month or 700 points a day). I think this is a pretty awesome sample size, though it is not truly random since places I play with my phone are over represented, places my phone is turned off or underground are excluded, and places I run are excluded since I don't carry my phone. Here are all the raw data points and path lines for reference:



I also calculated a kernel density surface of the points to better represent the areas with many overlapping points. This map is below:



UPDATE: Here is another set of maps I created with one full year of location history data from Vancouver:





I would be happy to make a map of YOUR location history for a small fee if you are interested - all I need is a copy of your Google Takeout file in KML format that you can download from here. Please let me know if you have any questions in a comment below. I think it would be radical if I could automate this process and build an app so people could create their own custom history maps with the touch of a button - get in touch if you have any ideas about this!

Read More »

20 Best Maps of 2014

Happy new year! In this post I have summarized my 20 favourite mapping projects from 2014. This has been an exciting year for me! I was promoted in my role at Golder Associates, working as a GIS Analyst and Cartographer on urban and environmental planning projects; however, I resigned in august and moved to Toronto to begin my Masters in Urban and Regional Planning at Ryerson University. I have continued to produce maps for this blog in my spare time using open data and I was recently hired to create maps for Dr. Richard Florida at the U of T Martin Prosperity Institute. I already have some exciting ideas for 2015, so consider subscribing to this blog to see more cool maps in the future!



 Vancouver Property Values

Metro Vancouver Commuter Challenge Trips




Comparative Study of Bicycle Infrastructure

Population Density in Metro Vancouver and Toronto

Average Home Value in Metro Vancouver

Median Income in Toronto

Visible Minority Population in Metro Vancouver and Toronto

Social Determinants of Health Research for Cowichan Valley

City of Vancouver Integrated Stormwater Management Plan Analysis

Skate Park Planning Community Consultation 

Assessment of Building Permits in Toronto

Assessment of Park Accessibility in Vancouver

Analysis of Dog Licences in Toronto and Most Popular Dog Names

Community Gardening in Vancouver and the Arbutus Corridor

Street Lamp Density in the City of Vancouver

Neighbourhood Design Evaluation of Cabbagetown

Environmental Assessment for Woodfibre LNG Proposal

Environmental Assessment for BURNCO Mine Proposal

Valentines Map

Walkability Map of Vancouver

Want More? Check out my "Thirteen Best Maps of 2013"

Read More »

Dogs of Toronto

I love dogs. I love data. I recently found an Excel file in the City of Toronto's Open Data Library that contains the number of pet licences per 3-digit postal code in 2013. Naturally, I wanted to SEE what the data looked like. The following three maps explore different dimensions of this dataset. First I explored the number of dogs per household, then the number of dogs per square kilometre of residential land, and finally the ratio of dogs to cats in each 3-digit postal code.










Overall, there are approximately 55,000 dogs and 25,000 cats registered in Toronto. It is important to note that not all pets are properly licenced, so these data may not be fully representative. In particular, cats may be under-represented due to lower licensing rates. For more open data exploration about pets, be sure to visit my previous post about the Dogs of Vancouver and don't hesitate to let me know if you have any questions!

Read More »

Divvy Bike Trips in Chicago

Last fall I had the pleasure of visiting Chicago to run my first marathon. While there, I also enjoyed a ride along the waterfront on a Divvy bike-share bike. A few months ago I was excited to learn that a year of trip data had been released as open data for a data visualization contest. Although I did not enter the contest (results are here), I still could not resist the opportunity to transform the .CSV files in to origin-destination maps. With Vancouver poised to launch its own bike-share system in the next year, I hope to learn some transferable lessons that may assist the implementation of our own bike share system.

A friend told me most Divvy trips were quite short, so I explored the data and was surprised to learn the mean trip length is about 20 minutes. Therefore I wanted to look at the travel patterns by trip length, so I decided to produce the map below that includes over 750,000 trips - about 250,000 trips on each frame.



I also added a new graphic below that shows the total minutes of physical activity generated and the average trip length per neighbourhood. The basic story is that people who live on the outskirts of town ride longer trips, but there is a lower cummulative time spent travelling as there are fewer trips. I am very interested in looking at the cummulative health benefit of all these bike trips...I am working with a friend who works with the City of Chicago Public Health Department and she will be writing a guest blog post soon.



Let me know if you would like me to look at any specific questions using this goldmine of big data?

Read More »

'WalkMap' Preview

I am in the home stretch of creating a new open source walkability dataset I am calling "WalkMap" based on open data obtained from Open Street Map (OSM). The key benefit of using the OSM data is it includes all urban walking paths and trails in addition to main roads. I am currently refining the processing model for the Greater Vancouver area and hope to make this data available for all of Canada in the next few months. Currently Walkscore (TM) provides a great user interface but their data are fairly coarse-grained and very expensive to obtain. Therefore, my work is based on a unique 100m processing grid and I am planning to provide both the methodology and outputs via an open source licence. My goal is to create one primary WalkMap Index, as well as specific measures of the following WalkMap components:

1. Walkable Area - The total area you can access by walking 800m along a road or trail in every possible direction 
2. Connectivity - The density of street street intersections 
3. Destinations - The number of shops, cafes or other businesses within walking distance. 
4. Infrastructure - The number of crosswalks, benches, water fountains, and other pedestrian infrastructure. 
5. Nature - Proportion of the walkable area that is natural vegetation, recreation or park space. 

I will also visualize my results on a simple mapping portal with cartography handled by MapBox, another exciting tool I am learning to use:


I have not designed a legend yet, but here is the basic story:

Green = More Walkable, Red =Less Walkable, No colour = Not Walkable

Please subscribe to this blog for future project updates or post a comment below.

Read More »